From d6d8b2dcde21476fa7e2c57223c2f0dfd95dd58c Mon Sep 17 00:00:00 2001 From: Simon Zhang Date: Mon, 18 Jul 2022 16:20:15 -0400 Subject: [PATCH] Added all experiments for Graph Extended Filtration Learning, readout ablation study, filtration convolution length experiment, and the standard GNN baseline. --- .idea/.gitignore | 8 + .idea/GraphExtendedFiltrationLearning.iml | 8 + .../inspectionProfiles/profiles_settings.xml | 6 + .idea/misc.xml | 4 + .idea/modules.xml | 8 + .idea/vcs.xml | 6 + 2cycles_average.sh | 8 + 2cycles_extpers.sh | 8 + 2cycles_max.sh | 8 + 2cycles_sum.sh | 8 + README.md | 52 + data/__pycache__/data.cpython-38.pyc | Bin 0 -> 10821 bytes data/__pycache__/utils.cpython-38.pyc | Bin 0 -> 1168 bytes data/data.py | 462 ++++++++ data/synthetic/Synthetic.py | 188 +++ .../__pycache__/Synthetic.cpython-38.pyc | Bin 0 -> 6932 bytes data/utils.py | 40 + dd_extpers.sh | 8 + dd_max.sh | 8 + dd_mean.sh | 8 + dd_sum.sh | 8 + external_experiments/sup_baseline/README.md | 29 + 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local storage ignored files +/dataSources/ +/dataSources.local.xml +# Editor-based HTTP Client requests +/httpRequests/ diff --git a/.idea/GraphExtendedFiltrationLearning.iml b/.idea/GraphExtendedFiltrationLearning.iml new file mode 100644 index 0000000..d0876a7 --- /dev/null +++ b/.idea/GraphExtendedFiltrationLearning.iml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 0000000..d1e22ec --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,4 @@ + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 0000000..fead218 --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100644 index 0000000..94a25f7 --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/2cycles_average.sh b/2cycles_average.sh new file mode 100755 index 0000000..37356da --- /dev/null +++ b/2cycles_average.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for iter in {0..0} + do + dataset="2cycles" + name=${dataset}-${iter} + python3 -m train.2cycles_train_eval --readout average --num_epochs 100 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} #&> imdb-multi-slurmfiles/slurm-${SLURM_JOB_ID}.out + done diff --git a/2cycles_extpers.sh b/2cycles_extpers.sh new file mode 100755 index 0000000..264d3d4 --- /dev/null +++ b/2cycles_extpers.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for iter in {0..0} + do + dataset="2cycles" + name=${dataset}-${iter} + python3 -m train.2cycles_train_eval --readout extph --num_epochs 100 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} #&> imdb-multi-slurmfiles/slurm-${SLURM_JOB_ID}.out + done diff --git a/2cycles_max.sh b/2cycles_max.sh new file mode 100755 index 0000000..0974ff1 --- /dev/null +++ b/2cycles_max.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for iter in {0..0} + do + dataset="2cycles" + name=${dataset}-${iter} + python3 -m train.2cycles_train_eval --readout max --num_epochs 100 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} #&> imdb-multi-slurmfiles/slurm-${SLURM_JOB_ID}.out + done diff --git a/2cycles_sum.sh b/2cycles_sum.sh new file mode 100755 index 0000000..63a74c8 --- /dev/null +++ b/2cycles_sum.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for iter in {0..0} + do + dataset="2cycles" + name=${dataset}-${iter} + python3 -m train.2cycles_train_eval --readout sum --num_epochs 100 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} #&> imdb-multi-slurmfiles/slurm-${SLURM_JOB_ID}.out + done diff --git a/README.md b/README.md new file mode 100644 index 0000000..f719748 --- /dev/null +++ b/README.md @@ -0,0 +1,52 @@ +#Graph Extended Filtration Learning + +## Summary +It is impossible for a standard message passing GNN with finite receptive field to distinguish arbitrary cycle lengths for graph classification. We address this problem by using extended persistence in the readout function. Extended persistence is a quantifiable multiscale method to capture the relative prominence of cycles and connected components. We also improve the computation of extended perisistence by using a link-cut tree data structure to dynamically maintain cycle information and introduce parallelism. + +## Installation +* python==3.9.1 +* torch==1.10.1 +* CUDA==11.2 +* GCC==7.5.0 +* torch-geometric==2.0.5 +* torch-scatter==2.0.9 +* torch-sparse==0.6.13 + +## Running Experiments +Replace "proteins" with any of dd/mutag/imdb-multi/molbace/molbbbp/2cycles/pinwheels below to run experiment on any dataset + +To run the proteins readout ablation experiment run the following commands (in parallel): + +``` +source proteins_extpers.sh + +source proteins_sum.sh + +source proteins_max.sh + +source proteins_average.sh +``` + +In general, change the --readout flag to the readout function of choice in the shell file. + +To run the proteins filtration convolution length experiment, run the following commands (in parallel): +``` +source proteinslen1_sup.sh + +source proteinslen2_sup.sh + +source proteinslen3_sup.sh + +source proteinslen4_sup.sh + +source proteinslen5_sup.sh +``` + +To run the standard message passing graph neural network baseline, run the command: + +``` +cd external_experiments/sup_baseline + +source run_sup_baseline.sh +``` + diff --git a/data/__pycache__/data.cpython-38.pyc b/data/__pycache__/data.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4e0660c3bf3c44d64a4cdf2ea25358fbc5396ee5 GIT binary patch literal 10821 zcmdT~+ix7#d7m>gJ3D(Pmp74;rL`p6TE~)X8Ez9tadjaj3zjH_q8IglHk(<;-)1qCvKwANQ$aB$$6e!yK0R;-oQ-D4c2J#OyZGry2GkYUN z*=~@RE;-+vbLPxhVIno+u<#K2l58Q-u`L7LV4_^-Lk7 z(spgEK3*6{I#yA&CP~%MP3gw%Tg3&Wv$}!H!d}6JeVF9EU#~_t<~J+R;@yB z&D_{3UlzsI`VG-+-4HJCG5j2Bp-~gdTMfDH%1Q~Dczw$$HEXq^bOZaw%0=hK<;BH< zxp?u)O9hMa*OtQjrE9%=FMsV?!M?F{eP!Xw;&MB+v@kz^W##0BD~sos-f3rg*=ski zu3S0!${U5~!eZf_YYWSPK0n`1T)8%Xpq~DepN1%bkSH$PlWv2%{8FW+J~)<6L^}MQRGx>E3h4>(x^zs zp>cQ?h{n>)V(sa0YJ1~9)vs0}neP86O>?X!f(#TULaVZEXqXsB7HVYF+=iG!`VkW| 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.synthetic import Synthetic +from .utils import my_collate + +POWERFUL_GNN_DATASET_NAMES = ["PTC_PGNN"] +TU_DORTMUND_DATASET_NAMES = [ + "NCI1", "PTC_MR", 'PTC_FM', + 'PTC_FR', 'PTC_MM', "PROTEINS", + "REDDIT-BINARY", "REDDIT-MULTI-5K", + "ENZYMES", "DD", "IMDB-BINARY", "IMDB-MULTI", "MUTAG", "COLLAB" +] +OGB_DATASET_NAMES= ['ogbg-molhiv', 'ogbg-ppa', 'ogbg-molpcba', 'ogbg-code2', + 'ogbg-molbace', + 'ogbg-molbbbp', + 'ogbg-molclintox', + 'ogbg-molmuv', + 'ogbg-molsider', + 'ogbg-moltox21', + 'ogbg-moltoxcast'] +SYNTHETIC_DATASET_NAMES= ['SimpleRings', 'PinWheels', 'Simple2Cycles'] +class SimpleDataset(torch.utils.data.Dataset): + def __init__(self, X, y): + assert isinstance(X, list) + self.data = X + self.y= y + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + return self.data[idx] + + def __iter__(self): + for i in range(len(self.data)): + yield self.data[i] + + +class Subset(torch.utils.data.Dataset): + def __init__(self, dataset, indices): + assert isinstance(indices, (list, tuple)) + self.ds = dataset + self.indices = tuple(indices) + self.y= dataset.y[indices] + assert len(indices) <= len(dataset) + + def __len__(self): + return len(self.indices) + + def __getitem__(self, idx): + return self.ds[self.indices[idx]] + +def load_powerfull_gnn_dataset_PTC(): + has_node_features = False + + dataset_name = "PTC" + path = "/home/pma/chofer/repositories/powerful-gnns/dataset/{}/{}.txt".format(dataset_name, dataset_name) + + with open(path, 'r') as f: + num_graphs = int(f.readline().strip()) + + data = [] + + graph_label_map = {} + node_lab_map = {} + + for i in range(num_graphs): + row = f.readline().strip().split() + num_nodes, graph_label = [int(w) for w in row] + + if graph_label not in graph_label_map: + graph_label_map[graph_label] = len(graph_label_map) + + graph_label = graph_label_map[graph_label] + + + nodes = [] + node_labs = [] + edges = [] + node_features = [] + + for node_id in range(num_nodes): + nodes.append(node_id) + + row = f.readline().strip().split() + + node_lab = int(row[0]) + + if node_lab not in node_lab_map: + node_lab_map[node_lab] = len(node_lab_map) + + node_labs.append(node_lab_map[node_lab]) + + num_neighbors = int(row[1]) + neighbors = [int(i) for i in row[2:num_neighbors+2]] + assert num_neighbors == len(neighbors) + + edges += [(node_id, neighbor_id) for neighbor_id in neighbors] + + if has_node_features: + node_features = [float(i) for i in row[(2 + num_neighbors):]] + assert len(node_features) != 0 + + # x = torch.tensor(node_features) if has_node_features else None + x = torch.tensor(node_labs, dtype=torch.long) + + edge_index = torch.tensor(edges, dtype=torch.long) + edge_index = edge_index.permute(1, 0) + tmp = edge_index.index_select(0, torch.tensor([1, 0])) + edge_index = torch.cat([edge_index, tmp], dim=1) + + y = torch.tensor([graph_label]) + + d = torch_geometric.data.Data( + x=x, + edge_index=edge_index, + y = y + ) + + d.num_nodes = num_nodes + + data.append(d) + + max_node_lab = max([d.x.max().item() for d in data]) + 1 + eye = torch.eye(max_node_lab, dtype=torch.long) + for d in data: + node_lab = eye.index_select(0, d.x) + d.x = node_lab + + ds = SimpleDataset(data) + ds.name = dataset_name + + return ds + + +def get_boundary_info(g): + + e = g.edge_index.permute(1, 0).sort(1)[0].tolist() + e = set([tuple(ee) for ee in e]) + return torch.tensor([ee for ee in e], dtype=torch.long) + +def enhance_SYNTHETICDataset(ds): + X = [] + targets = [] + + max_degree_by_graph = [] + num_nodes = [] + num_edges = [] + + for d in ds: + #print("d.y: ", d.y) + #print("d.x", d.x) + targets.append(d.y) + + # d.edge_idx = d.edge_idx.permute(1, 0) + # idx = torch.empty(d.edge_idx.size(0)).uniform_(0, 1) + # d.edge_idx = d.edge_idx[torch.where(idx >= 0.01)].permute(1, 0) + + boundary_info = get_boundary_info(d) + d.boundary_info = boundary_info + + num_nodes.append(d.num_nodes) + num_edges.append(boundary_info.size(0)) + + degree = torch.zeros(d.num_nodes, dtype=torch.long) + + for k, v in Counter(d.boundary_info.flatten().tolist()).items(): + degree[k] = v + max_degree_by_graph.append(degree.max().item()) + + d.node_deg = degree + X.append(d) + + max_node_deg = max(max_degree_by_graph) + + num_node_lab = None + #print("X[0]: ",X[0]) + #print("X[0].x:", X[0].x) + if hasattr(X[0], 'x') and X[0].x is not None: + print("USING NODE LABEL") + all_node_lab = [] + for d in X: + assert d.x.sum() == d.x.size(0) # really one hot encoded? + node_lab = d.x.argmax(1).tolist() + d.node_lab = node_lab + all_node_lab += node_lab + + all_node_lab = set(all_node_lab) + num_node_lab = len(all_node_lab) + label_map = {k: i for i, k in enumerate(sorted(all_node_lab))} + + for d in X: # numbers from consecutive indices represent the one hot encodings now + d.node_lab = [label_map[f] for f in d.node_lab] + d.node_lab = torch.tensor(d.node_lab, dtype=torch.long) + else: + for d in X: + d.node_lab = None + + new_ds = SimpleDataset(X, ds.data.y) + + new_ds.max_node_deg = max_node_deg + new_ds.avg_num_nodes = np.mean(num_nodes) + new_ds.avg_num_edges = np.mean(num_edges) + new_ds.num_classes = len(set(targets)) + new_ds.num_node_lab = num_node_lab + + return new_ds + +def enhance_PYGYDataset(ds): + X = [] + targets = [] + + max_degree_by_graph = [] + num_nodes = [] + num_edges = [] + + #dl= torch.utils.data.DataLoader(ds, batch_size=1, shuffle= False) + + for d in ds: + #print(d.y) + targets.append(d.y.item()) + + # d.edge_idx = d.edge_index.permute(1, 0) + # idx = torch.empty(d.edge_index.size(0)).uniform_(0, 1) + # d.edge_idx = d.edge_index[torch.where(idx >= 0.01)].permute(1, 0) + + boundary_info = get_boundary_info(d) + d.boundary_info = boundary_info + + num_nodes.append(d.num_nodes) + num_edges.append(boundary_info.size(0)) + + degree = torch.zeros(d.num_nodes, dtype=torch.long) + + for k, v in Counter(d.boundary_info.flatten().tolist()).items(): + degree[k] = v + max_degree_by_graph.append(degree.max().item()) + + d.node_deg = degree + X.append(d) + + max_node_deg = max(max_degree_by_graph) + + + for d,dd in zip(X,ds): + if dd.x is not None: + d.node_lab= torch.tensor(dd.x.tolist()) + else: + d.node_lab= torch.tensor([]) + if X[0].x is not None: + num_node_lab= (X[0].x.shape[1]) + else: + num_node_lab= 0 + # num_node_lab = len(set([scalar for d in X for node_repr in d.node_lab for scalar in node_repr ])) + # if hasattr(X[0], 'x') and X[0].x is not None: + # + # all_node_lab = [] + # for d in X: + # assert d.x.sum() == d.x.size(0) # really one hot encoded? + # node_lab = d.x.argmax(1).tolist() + # d.node_lab = node_lab + # all_node_lab += node_lab + # + # all_node_lab = set(all_node_lab) + # num_node_lab = len(all_node_lab) + # label_map = {k: i for i, k in enumerate(sorted(all_node_lab))} + # + # for d in X: + # d.node_lab = [label_map[f] for f in d.node_lab] + # d.node_lab = torch.tensor(d.node_lab, dtype=torch.long) + # else: + # for d in X: + # d.node_lab = None + + new_ds = SimpleDataset(X, ds.data.y) + + new_ds.max_node_deg = max_node_deg + new_ds.avg_num_nodes = np.mean(num_nodes) + new_ds.avg_num_edges = np.mean(num_edges) + new_ds.num_classes = len(set(targets)) + new_ds.num_node_lab = num_node_lab + + return new_ds + +def enhance_TUDataset(ds): + + X = [] + targets = [] + + max_degree_by_graph = [] + num_nodes = [] + num_edges = [] + + for d in ds: + #print("d.y: ", d.y) + #print("d.x: ", d.x) + #print("d.edge_attr: ", d.edge_attr) + targets.append(d.y.item()) + + #d.edge_idx = d.edge_index.permute(1, 0) + #idx = torch.empty(d.edge_index.size(0)).uniform_(0, 1) + #d.edge_idx = d.edge_index[torch.where(idx >= 0.01)].permute(1, 0) + + boundary_info = get_boundary_info(d) + d.boundary_info = boundary_info + + num_nodes.append(d.num_nodes) + num_edges.append(boundary_info.size(0)) + + degree = torch.zeros(d.num_nodes, dtype=torch.long) + + for k, v in Counter(d.boundary_info.flatten().tolist()).items(): + degree[k] = v + max_degree_by_graph.append(degree.max().item()) + + d.node_deg = degree + X.append(d) + + max_node_deg = max(max_degree_by_graph) + + num_node_lab = None + + if hasattr(X[0], 'x') and X[0].x is not None: + if X[0].x.sum() == X[0].x.size(0): + all_node_lab = [] + for d in X: + assert d.x.sum() == d.x.size(0) # really one hot encoded? + #if d.x.sum() == d.x.size(0): # really one hot encoded? + node_lab = d.x.argmax(1).tolist() + d.node_lab = node_lab + all_node_lab += node_lab + all_node_lab = set(all_node_lab) + num_node_lab = len(all_node_lab) + label_map = {k: i for i, k in enumerate(sorted(all_node_lab))} + + for d in X:# numbers from consecutive indices represent the one hot encodings now + d.node_lab = [label_map[f] for f in d.node_lab] + d.node_lab = torch.tensor(d.node_lab, dtype=torch.long) + else: + for d in X: + d.node_lab= d + else: + for d in X: + d.node_lab = None + + new_ds = SimpleDataset(X, ds.data.y) + + new_ds.max_node_deg = max_node_deg + new_ds.avg_num_nodes = np.mean(num_nodes) + new_ds.avg_num_edges = np.mean(num_edges) + new_ds.num_classes = len(set(targets)) + new_ds.num_node_lab = num_node_lab + + return new_ds + + +def dataset_factory(dataset_name, verbose=True): + if dataset_name in TU_DORTMUND_DATASET_NAMES: + + path = 'data/{}/'.format(dataset_name) + dataset = TUDataset(path, name=dataset_name) + + elif dataset_name in POWERFUL_GNN_DATASET_NAMES: + if dataset_name == "PTC_PGNN": + dataset = load_powerfull_gnn_dataset_PTC() + elif dataset_name in OGB_DATASET_NAMES: + dataset= PygGraphPropPredDataset(name = dataset_name) + elif dataset_name in SYNTHETIC_DATASET_NAMES: + if dataset_name=='SimpleRings': + dataset= Synthetic.SimpleRings(root=".") + elif dataset_name=='PinWheels': + dataset= Synthetic.PinWheels(root=".") + elif dataset_name=='Simple2Cycles': + dataset= Synthetic.SimpleTwoCycles(root='.') + else: + raise ValueError("dataset_name not in {}".format(TU_DORTMUND_DATASET_NAMES + POWERFUL_GNN_DATASET_NAMES+OGB_DATASET_NAMES+SYNTHETIC_DATASET_NAMES)) + ds_name = dataset.name + if dataset_name in OGB_DATASET_NAMES: + dataset= enhance_PYGYDataset(dataset) + elif dataset_name in SYNTHETIC_DATASET_NAMES: + dataset= enhance_SYNTHETICDataset(dataset) + else: + dataset = enhance_TUDataset(dataset) + + if verbose: + print("# Dataset: ", ds_name) + print('# num samples: ', len(dataset)) + print('# num classes: ', dataset.num_classes) + print('#') + print('# max node degree: ', dataset.max_node_deg) + print('# num node label ', dataset.num_node_lab) + print('#') + print('# avg number of nodes: ', dataset.avg_num_nodes) + print('# avg number of edges: ', dataset.avg_num_edges) + + + return dataset + + +def train_test_val_split( + dataset, + seed=0, + n_splits=10, + verbose=True, + validation_ratio=0.0): + + skf = StratifiedKFold( + n_splits=n_splits, + shuffle = True,#True, + random_state = seed, + ) + + targets = [x.y.item() for x in dataset] + split_idx = list(skf.split(np.zeros(len(dataset)), targets)) + + if verbose: + print('# num splits: ', len(split_idx)) + print('# validation ratio: ', validation_ratio) + + split_ds = [] + split_i = [] + for train_i, test_i in split_idx: + not_test_i, test_i = train_i.tolist(), test_i.tolist() + + if validation_ratio == 0.0: + validation_i = [] + train_i = not_test_i + + else: + skf = StratifiedShuffleSplit( + n_splits=1, + random_state = seed, + test_size=validation_ratio + ) + + targets = [dataset[i].y.item() for i in not_test_i] + train_i, validation_i = list(skf.split(np.zeros(len(not_test_i)), targets))[0] + train_i, validation_i = train_i.tolist(), validation_i.tolist() + + # We need the indices w.r.t. the original dataset + # not w.r.t. the current train fold ... + train_i = [not_test_i[j] for j in train_i] + validation_i = [not_test_i[j] for j in validation_i] + + assert len(set(train_i).intersection(set(validation_i))) == 0 + + train = Subset(dataset, train_i) + test = Subset(dataset, test_i) + validation = Subset(dataset, validation_i) + + assert sum([len(train), len(test), len(validation)]) == len(dataset) + + split_ds.append((train, test, validation)) + split_i.append((train_i, test_i, validation_i)) + + return split_ds, split_i diff --git a/data/synthetic/Synthetic.py b/data/synthetic/Synthetic.py new file mode 100644 index 0000000..b54fe6a --- /dev/null +++ b/data/synthetic/Synthetic.py @@ -0,0 +1,188 @@ +import torch +from torch_geometric.data import Dataset, download_url +from torch_geometric.data import Data, InMemoryDataset +from random import randrange +import numpy as np + +def fix_seed(seed): + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.backends.cudnn.deterministic = True + +class SimpleTwoCycles(InMemoryDataset):#100 + def __init__(self, root, num_graphs= 400, transform=None, pre_transform=None):#1000 + self.num_graphs= num_graphs + self.name= "SimpleTwoCycles" + super().__init__(root, transform, pre_transform) + self.data, self.slices = torch.load(self.processed_paths[0]) + fix_seed(0) + @property + def raw_file_names(self): + return ['twocycles_'+str(i)+'.pt' for i in range(self.num_graphs)] + + @property + def processed_file_names(self): + return ['twocycle_data.pt'] + + #def download(self): + # # Download to `self.raw_dir`. + # path = download_url(url, self.raw_dir) + # ... + + def process(self): + data_list= [] + idx = 0 + for n_ in range(int(self.num_graphs)): + if n_ % 2==0: + n= randrange(500,506)#randrange(100,106) + #for raw_path in self.raw_paths: + # Read data from `raw_path`. + data = Data(x= torch.tensor([[1,0]]*n), + edge_index=torch.tensor([[i,i+1] for i in range(0,103)]+[[103,0]]+[[i,i+1] for i in range(104,n-1)]+[[n-1,104]]).transpose(0,1),y= torch.tensor([1])) + + data.y= torch.Tensor([1]) + else: + n = randrange(600,601)#randrange(10, 11) + k= int(n/2) + data = Data(x= torch.tensor([[1,0]]*n, dtype= torch.float), + edge_index=torch.tensor([[i,i+1] for i in range(0,k-1)]+ [[k-1,0]]+ [[i,i+1] for i in range(k,n-1)]+ [[n-1,k]]).transpose(0, 1), + y=torch.tensor([0])) + data.y = torch.Tensor([0]) + + # print(data) + data.num_nodes = n + if self.pre_filter is not None and not self.pre_filter(data): + continue + + if self.pre_transform is not None: + data = self.pre_transform(data) + + idx += 1 + data_list.append(data) + #print(data_list) + data,slices= self.collate(data_list) + torch.save((data,slices), self.processed_paths[0]) + + def len(self): + return self.num_graphs + +class PinWheels(InMemoryDataset): + def __init__(self, root, num_graphs= 100, transform=None, pre_transform=None): + self.num_graphs= num_graphs + self.name= "PinWheels" + super().__init__(root, transform, pre_transform) + self.data, self.slices = torch.load(self.processed_paths[0]) + fix_seed(0) + @property + def raw_file_names(self): + return ['pinwheels_'+str(i)+'.pt' for i in range(self.num_graphs)] + + @property + def processed_file_names(self): + return ['pinwheels_data.pt'] + + #def download(self): + # # Download to `self.raw_dir`. + # path = download_url(url, self.raw_dir) + # ... + + def process(self): + data_list= [] + idx = 0 + for n in range(int(self.num_graphs)): + if n % 2==0: + tendrils_per_node= randrange(10,13) + n= tendrils_per_node*6+6 + #for raw_path in self.raw_paths: + # Read data from `raw_path`. + data = Data(x= torch.tensor([[1,0]]*n, dtype= torch.float), + #x= torch.tensor([[1,0,0,0]]*3+[[0,1,0,0]]*3+[[0,0,1,0] if k in [0,1,2] else [0,0,0,1] for k in range(0,6) for i in range(1,tendrils_per_node+1) ]), + edge_index=torch.tensor([[0,1],[1,2],[2,0],[3,4],[4,5],[5,3]]+[[k,6*i+k] for i in range(1,tendrils_per_node+1) for k in range(0,6)]).transpose(0,1),y= torch.tensor([1])) + + data.y= torch.Tensor([1]) + else: + tendrils_per_node = randrange(10,13) + n = tendrils_per_node * 6 + 6 + data = Data(x= torch.tensor([[1,0]]*n), + #x= torch.tensor([[1,0,0,0]]*3+[[0,1,0,0]]*3+[[0,0,1,0] if k in [0,1,2] else [0,0,0,1] for k in range(0,6) for i in range(1,tendrils_per_node+1) ]), + edge_index=torch.tensor( + [[0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 0]] + [[k, 6 * i + k] for i in + range(1, tendrils_per_node + 1) for k in + range(0, 6)]).transpose(0, 1), + y=torch.tensor([0])) + data.y = torch.Tensor([0]) + + # print(data) + data.num_nodes = n + if self.pre_filter is not None and not self.pre_filter(data): + continue + + if self.pre_transform is not None: + data = self.pre_transform(data) + + idx += 1 + data_list.append(data) + # print(data_list) + data,slices= self.collate(data_list) + torch.save((data,slices), self.processed_paths[0]) + + def len(self): + return self.num_graphs +class SimpleRings(InMemoryDataset): + def __init__(self, root, num_graphs= 100, transform=None, pre_transform=None): + self.num_graphs= num_graphs + self.name= "SimpleRings" + super().__init__(root, transform, pre_transform) + self.data, self.slices = torch.load(self.processed_paths[0]) + + @property + def raw_file_names(self): + return ['ring_'+str(i)+'.pt' for i in range(self.num_graphs)] + + @property + def processed_file_names(self): + return ['ring_data.pt'] + + #def download(self): + # # Download to `self.raw_dir`. + # path = download_url(url, self.raw_dir) + # ... + + def process(self): + data_list= [] + idx = 0 + for n in range(int(self.num_graphs)): + if n % 2==0: + n=int(n/2) +6 + #for raw_path in self.raw_paths: + # Read data from `raw_path`. + k=randrange(2,n-4+1) + data = Data(x= torch.tensor([[1,0]]*n), edge_index=torch.tensor([[i,i+1] for i in range(k)]+[[k,0]]+[[i,i+1] for i in range(k+1,n-1)]+[[n-1,k+1]]).transpose(0,1),y= torch.tensor([0])) + + data.y= torch.Tensor([0]) + print("data0; ", data) + print("data0.y", data.y) + print("data0.x: ", data.x) + else: + n= int((n-1)/2)+6 + data= Data(x=torch.tensor([[1,0]]*n), edge_index=torch.tensor([[i,i+1] for i in range(n-1)]+[[n-1,0]]).transpose(0,1), y=torch.tensor([1])) + data.y = torch.Tensor([1]) + + print(data) + data.num_nodes = n + if self.pre_filter is not None and not self.pre_filter(data): + continue + + if 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iter in {0..0} + do + dataset="dd" + name=${dataset}-${iter} + python3 -m train.dd_train_eval --readout max --filt_conv_number 2 --num_epochs 100 --batch_size 128 --use_raw_node_label True --output_dir ./results --device 0 --exp_name ${name} #&> dd-max-len-2-slurm-${SLURM_JOB_ID}.out + done diff --git a/dd_mean.sh b/dd_mean.sh new file mode 100755 index 0000000..5a70c69 --- /dev/null +++ b/dd_mean.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for iter in {0..0} + do + dataset="dd" + name=${dataset}-${iter} + python3 -m train.dd_train_eval --readout average --filt_conv_number 2 --num_epochs 100 --batch_size 128 --use_raw_node_label True --output_dir ./results --device 0 --exp_name ${name} #&> dd-average-len-2-slurm-${SLURM_JOB_ID}.out + done diff --git a/dd_sum.sh b/dd_sum.sh new file mode 100755 index 0000000..d98eb7c --- /dev/null +++ b/dd_sum.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for iter in {0..0} + do + dataset="dd" + name=${dataset}-${iter} + python3 -m train.dd_train_eval --readout sum --filt_conv_number 2 --num_epochs 100 --batch_size 128 --use_raw_node_label True --output_dir ./results --device 0 --exp_name ${name} #&> dd-sum-len-2-slurm-${SLURM_JOB_ID}.out + done diff --git a/external_experiments/sup_baseline/README.md b/external_experiments/sup_baseline/README.md new file mode 100644 index 0000000..0ff5fee --- /dev/null +++ b/external_experiments/sup_baseline/README.md @@ -0,0 +1,29 @@ +# Graph Classification + +Evaluation script for various methods on [common benchmark datasets](http://graphkernels.cs.tu-dortmund.de) via 10-fold cross validation, where a training fold is randomly sampled to serve as a validation set. +Hyperparameter selection is performed for the number of hidden units and the number of layers with respect to the validation set: + +* **[GCN](https://github.com/pyg-team/pytorch_geometric/blob/master/benchmark/kernel/gcn.py)** +* **[GraphSAGE](https://github.com/pyg-team/pytorch_geometric/blob/master/benchmark/kernel/graph_sage.py)** +* **[GIN](https://github.com/pyg-team/pytorch_geometric/blob/master/benchmark/kernel/gin.py)** +* **[Graclus](https://github.com/pyg-team/pytorch_geometric/blob/master/benchmark/kernel/graclus.py)** +* **[Top-K Pooling](https://github.com/pyg-team/pytorch_geometric/blob/master/benchmark/kernel/top_k.py)** +* **[SAG Pooling](https://github.com/pyg-team/pytorch_geometric/blob/master/benchmark/kernel/sag_pool.py)** +* **[DiffPool](https://github.com/pyg-team/pytorch_geometric/blob/master/benchmark/kernel/diff_pool.py)** +* **[EdgePool](https://github.com/pyg-team/pytorch_geometric/blob/master/benchmark/kernel/edge_pool.py)** +* **[GlobalAttention](https://github.com/pyg-team/pytorch_geometric/blob/master/benchmark/kernel/global_attention.py)** +* **[Set2Set](https://github.com/pyg-team/pytorch_geometric/blob/master/benchmark/kernel/set2set.py)** +* **[SortPool](https://github.com/pyg-team/pytorch_geometric/blob/master/benchmark/kernel/sort_pool.py)** +* **[ASAPool](https://github.com/pyg-team/pytorch_geometric/blob/master/benchmark/kernel/asap.py)** + +Run (or modify) the whole test suite via + +``` +$ python main.py +``` + +For more comprehensive time-measurement and memory usage information, you may use + +``` +$ python main_performance.py +``` diff --git a/external_experiments/sup_baseline/Synthetic.py b/external_experiments/sup_baseline/Synthetic.py new file mode 100644 index 0000000..71ac84b --- /dev/null +++ b/external_experiments/sup_baseline/Synthetic.py @@ -0,0 +1,197 @@ +import os.path as osp + +import torch +from torch_geometric.data import Dataset, download_url +from torch_geometric.data import Data, InMemoryDataset +from random import randrange + + +class SimpleTwoCycles(InMemoryDataset): + def __init__(self, root, num_graphs= 1000, transform=None, pre_transform=None): + self.num_graphs= num_graphs + self.name= "SimpleTwoCycles" + super().__init__(root, transform, pre_transform) + self.data, self.slices = torch.load(self.processed_paths[0]) + + @property + def raw_file_names(self): + return ['twocycles_'+str(i)+'.pt' for i in range(self.num_graphs)] + + @property + def processed_file_names(self): + return ['twocycles_data.pt'] + + #def download(self): + # # Download to `self.raw_dir`. + # path = download_url(url, self.raw_dir) + # ... + + def process(self): + data_list= [] + idx = 0 + for n in range(int(self.num_graphs)): + if n % 2==0: + n= randrange(53,63) + #for raw_path in self.raw_paths: + # Read data from `raw_path`. + data = Data(x= torch.tensor([[1,0]]*n).float(), + edge_index=torch.tensor([[0,1],[1,2],[2,0]]+[[i,i+1] for i in range(3,n-1)]+[[n-1,3]]).transpose(0,1),y= torch.tensor([1]).float()) + + data.y= torch.Tensor([1]).float() + print("data0; ", data) + print("data0.y", data.y) + print("data0.x: ", data.x) + else: + n = randrange(53, 63) + k= int(n/2) + data = Data(x= torch.tensor([[1,0]]*n).float(), + edge_index=torch.tensor([[i,i+1] for i in range(0,k-1)]+ [[k-1,0]]+ [[i,i+1] for i in range(k,n-1)]+ [[n-1,k]]).transpose(0, 1), + y=torch.tensor([0]).float()) + data.y = torch.Tensor([0]).float() + + print(data) + data.num_nodes = n + if self.pre_filter is not None and not self.pre_filter(data): + continue + + if self.pre_transform is not None: + data = self.pre_transform(data) + + #torch.save(data, osp.join(self.processed_dir, f'data_{idx}.pt')) + idx += 1 + data_list.append(data) + print(data_list) + data,slices= self.collate(data_list) + torch.save((data,slices), self.processed_paths[0]) + + def len(self): + return self.num_graphs + +class SimpleRegular(InMemoryDataset): + def __init__(self, root, num_graphs= 1000, transform=None, pre_transform=None): + self.num_graphs= num_graphs + self.name= "SimpleRegular" + super().__init__(root, transform, pre_transform) + self.data, self.slices = torch.load(self.processed_paths[0]) + + @property + def raw_file_names(self): + return ['regular_'+str(i)+'.pt' for i in range(self.num_graphs)] + + @property + def processed_file_names(self): + return ['regular_data.pt'] + + #def download(self): + # # Download to `self.raw_dir`. + # path = download_url(url, self.raw_dir) + # ... + + def process(self): + data_list= [] + idx = 0 + for n in range(int(self.num_graphs)): + if n % 2==0: + tendrils_per_node= randrange(10,13) + n= tendrils_per_node*6+6 + #for raw_path in self.raw_paths: + # Read data from `raw_path`. + data = Data(x= torch.tensor([[1,0]]*n).float(), + #x= torch.tensor([[1,0,0,0]]*3+[[0,1,0,0]]*3+[[0,0,1,0] if k in [0,1,2] else [0,0,0,1] for k in range(0,6) for i in range(1,tendrils_per_node+1) ]), + edge_index=torch.tensor([[0,1],[1,2],[2,0],[3,4],[4,5],[5,3]]+[[k,6*i+k] for i in range(1,tendrils_per_node+1) for k in range(0,6)]).transpose(0,1),y= torch.tensor([1]).float()) + + data.y= torch.Tensor([1]).float() + print("data0; ", data) + print("data0.y", data.y) + print("data0.x: ", data.x) + else: + tendrils_per_node = randrange(10,13) + n = tendrils_per_node * 6 + 6 + data = Data(x= torch.tensor([[1,0]]*n).float(), + #x= torch.tensor([[1,0,0,0]]*3+[[0,1,0,0]]*3+[[0,0,1,0] if k in [0,1,2] else [0,0,0,1] for k in range(0,6) for i in range(1,tendrils_per_node+1) ]), + edge_index=torch.tensor( + [[0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 0]] + [[k, 6 * i + k] for i in + range(1, tendrils_per_node + 1) for k in + range(0, 6)]).transpose(0, 1), + y=torch.tensor([0]).float()) + data.y = torch.Tensor([0]).float() + + print(data) + data.num_nodes = n + if self.pre_filter is not None and not self.pre_filter(data): + continue + + if self.pre_transform is not None: + data = self.pre_transform(data) + + #torch.save(data, osp.join(self.processed_dir, f'data_{idx}.pt')) + idx += 1 + data_list.append(data) + print(data_list) + data,slices= self.collate(data_list) + torch.save((data,slices), self.processed_paths[0]) + + def len(self): + return self.num_graphs +class SimpleRings(InMemoryDataset): + def __init__(self, root, num_graphs= 100, transform=None, pre_transform=None): + self.num_graphs= num_graphs + self.name= "SimpleRings" + super().__init__(root, transform, pre_transform) + self.data, self.slices = torch.load(self.processed_paths[0]) + + @property + def raw_file_names(self): + return ['rings_'+str(i)+'.pt' for i in range(self.num_graphs)] + + @property + def processed_file_names(self): + return ['rings_data.pt'] + + #def download(self): + # # Download to `self.raw_dir`. + # path = download_url(url, self.raw_dir) + # ... + + def process(self): + data_list= [] + idx = 0 + for n in range(int(self.num_graphs)): + if n % 2==0: + n=int(n/2) +6 + #for raw_path in self.raw_paths: + # Read data from `raw_path`. + k=randrange(2,n-4+1) + data = Data(x= torch.tensor([[1,0]]*n), edge_index=torch.tensor([[i,i+1] for i in range(k)]+[[k,0]]+[[i,i+1] for i in range(k+1,n-1)]+[[n-1,k+1]]).transpose(0,1),y= torch.tensor([0])) + + data.y= torch.Tensor([0]) + print("data0; ", data) + print("data0.y", data.y) + print("data0.x: ", data.x) + else: + n= int((n-1)/2)+6 + data= Data(x=torch.tensor([[1,0]]*n), edge_index=torch.tensor([[i,i+1] for i in range(n-1)]+[[n-1,0]]).transpose(0,1), y=torch.tensor([1])) + data.y = torch.Tensor([1]) + + print(data) + data.num_nodes = n + if self.pre_filter is not None and not self.pre_filter(data): + continue + + if self.pre_transform is not None: + data = self.pre_transform(data) + + #torch.save(data, osp.join(self.processed_dir, f'data_{idx}.pt')) + idx += 1 + data_list.append(data) + print(data_list) + data,slices= self.collate(data_list) + torch.save((data,slices), self.processed_paths[0]) + + def len(self): + return self.num_graphs + + # def get(self, idx): + # data = torch.load(osp.join(self.processed_dir, f'data_{idx}.pt')) + # + # return data \ No newline at end of file diff --git a/external_experiments/sup_baseline/__init__.py b/external_experiments/sup_baseline/__init__.py new file mode 100644 index 0000000..afe8fca --- /dev/null +++ b/external_experiments/sup_baseline/__init__.py @@ -0,0 +1,7 @@ +from 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a/external_experiments/sup_baseline/asap.py b/external_experiments/sup_baseline/asap.py new file mode 100644 index 0000000..74d7146 --- /dev/null +++ b/external_experiments/sup_baseline/asap.py @@ -0,0 +1,56 @@ +import torch +import torch.nn.functional as F +from torch.nn import Linear +from torch_geometric.nn import (ASAPooling, GraphConv, global_mean_pool, + JumpingKnowledge) + + +class ASAP(torch.nn.Module): + def __init__(self, dataset, num_layers, hidden, ratio=0.8, dropout=0): + super().__init__() + self.conv1 = GraphConv(dataset.num_features, hidden, aggr='mean') + self.convs = torch.nn.ModuleList() + self.pools = torch.nn.ModuleList() + self.convs.extend([ + GraphConv(hidden, hidden, aggr='mean') + for i in range(num_layers - 1) + ]) + self.pools.extend([ + ASAPooling(hidden, ratio, dropout=dropout) + for i in range((num_layers) // 2) + ]) + self.jump = JumpingKnowledge(mode='cat') + self.lin1 = Linear(num_layers * hidden, hidden) + self.lin2 = Linear(hidden, dataset.num_classes) + + def reset_parameters(self): + self.conv1.reset_parameters() + for conv in self.convs: + conv.reset_parameters() + for pool in self.pools: + pool.reset_parameters() + self.lin1.reset_parameters() + self.lin2.reset_parameters() + + def forward(self, data): + x, edge_index, batch = data.x, data.edge_index, data.batch + edge_weight = None + x = F.relu(self.conv1(x, edge_index)) + xs = [global_mean_pool(x, batch)] + for i, conv in enumerate(self.convs): + x = conv(x=x, edge_index=edge_index, edge_weight=edge_weight) + x = F.relu(x) + xs += [global_mean_pool(x, batch)] + if i % 2 == 0 and i < len(self.convs) - 1: + pool = self.pools[i // 2] + x, edge_index, edge_weight, batch, _ = pool( + x=x, edge_index=edge_index, edge_weight=edge_weight, + batch=batch) + x = self.jump(xs) + x = F.relu(self.lin1(x)) + x = F.dropout(x, p=0.5, training=self.training) + x = self.lin2(x) + return F.log_softmax(x, dim=-1) + + def __repr__(self): + return self.__class__.__name__ diff --git a/external_experiments/sup_baseline/datasets.py b/external_experiments/sup_baseline/datasets.py new file mode 100644 index 0000000..a12d623 --- /dev/null +++ b/external_experiments/sup_baseline/datasets.py @@ -0,0 +1,74 @@ +import os.path as osp + +import torch +from torch_geometric.datasets import TUDataset +from torch_geometric.utils import degree +import torch_geometric.transforms as T +import Synthetic + +class NormalizedDegree(object): + def __init__(self, mean, std): + self.mean = mean + self.std = std + + def __call__(self, data): + deg = degree(data.edge_index[0], dtype=torch.float) + deg = (deg - self.mean) / self.std + data.x = deg.view(-1, 1) + return data + +SYNTHETICS= ['SimpleRings', 'SimpleRegular', 'SimpleTwoCycles'] + +def get_dataset(name, sparse=True, cleaned=False): + path = osp.join(osp.dirname(osp.realpath(__file__)), '../..', 'data', name) + if name in SYNTHETICS: + if name == 'SimpleRings': + dataset = Synthetic.SimpleRings(root=".") + elif name == 'SimpleRegular': + dataset = Synthetic.SimpleRegular(root=".") + elif name == 'SimpleTwoCycles': + dataset = Synthetic.SimpleTwoCycles(root='.') + else: + dataset = TUDataset(path, name, cleaned=cleaned) + + dataset.data.edge_attr = None + + if dataset.data.x is None: + max_degree = 0 + degs = [] + for data in dataset: + degs += [degree(data.edge_index[0], dtype=torch.long)] + max_degree = max(max_degree, degs[-1].max().item()) + + if max_degree < 1000: + dataset.transform = T.OneHotDegree(max_degree) + else: + deg = torch.cat(degs, dim=0).to(torch.float) + mean, std = deg.mean().item(), deg.std().item() + dataset.transform = NormalizedDegree(mean, std) + + if not sparse: + num_nodes = max_num_nodes = 0 + for data in dataset: + num_nodes += data.num_nodes + max_num_nodes = max(data.num_nodes, max_num_nodes) + + # Filter out a few really large graphs in order to apply DiffPool. + if name == 'REDDIT-BINARY': + num_nodes = min(int(num_nodes / len(dataset) * 1.5), max_num_nodes) + else: + num_nodes = min(int(num_nodes / len(dataset) * 5), max_num_nodes) + + indices = [] + for i, data in enumerate(dataset): + if data.num_nodes <= num_nodes: + indices.append(i) + dataset = dataset.copy(torch.tensor(indices)) + + if dataset.transform is None: + dataset.transform = T.ToDense(num_nodes) + else: + dataset.transform = T.Compose( + [dataset.transform, T.ToDense(num_nodes)]) + + return dataset diff --git a/external_experiments/sup_baseline/diff_pool.py b/external_experiments/sup_baseline/diff_pool.py new file mode 100644 index 0000000..2a36a02 --- /dev/null +++ b/external_experiments/sup_baseline/diff_pool.py @@ -0,0 +1,85 @@ +from math import ceil + +import torch +import torch.nn.functional as F +from torch.nn import Linear +from torch_geometric.nn import DenseSAGEConv, dense_diff_pool, JumpingKnowledge + + +class Block(torch.nn.Module): + def __init__(self, in_channels, hidden_channels, out_channels, mode='cat'): + super().__init__() + + self.conv1 = DenseSAGEConv(in_channels, hidden_channels) + self.conv2 = DenseSAGEConv(hidden_channels, out_channels) + self.jump = JumpingKnowledge(mode) + if mode == 'cat': + self.lin = Linear(hidden_channels + out_channels, out_channels) + else: + self.lin = Linear(out_channels, out_channels) + + def reset_parameters(self): + self.conv1.reset_parameters() + self.conv2.reset_parameters() + self.lin.reset_parameters() + + def forward(self, x, adj, mask=None): + x1 = F.relu(self.conv1(x, adj, mask)) + x2 = F.relu(self.conv2(x1, adj, mask)) + return self.lin(self.jump([x1, x2])) + + +class DiffPool(torch.nn.Module): + def __init__(self, dataset, num_layers, hidden, ratio=0.25): + super().__init__() + + num_nodes = ceil(ratio * dataset[0].num_nodes) + self.embed_block1 = Block(dataset.num_features, hidden, hidden) + self.pool_block1 = Block(dataset.num_features, hidden, num_nodes) + + self.embed_blocks = torch.nn.ModuleList() + self.pool_blocks = torch.nn.ModuleList() + for i in range((num_layers // 2) - 1): + num_nodes = ceil(ratio * num_nodes) + self.embed_blocks.append(Block(hidden, hidden, hidden)) + self.pool_blocks.append(Block(hidden, hidden, num_nodes)) + + self.jump = JumpingKnowledge(mode='cat') + self.lin1 = Linear((len(self.embed_blocks) + 1) * hidden, hidden) + self.lin2 = Linear(hidden, dataset.num_classes) + + def reset_parameters(self): + self.embed_block1.reset_parameters() + self.pool_block1.reset_parameters() + for embed_block, pool_block in zip(self.embed_blocks, + self.pool_blocks): + embed_block.reset_parameters() + pool_block.reset_parameters() + self.jump.reset_parameters() + self.lin1.reset_parameters() + self.lin2.reset_parameters() + + def forward(self, data): + x, adj, mask = data.x, data.adj, data.mask + + s = self.pool_block1(x, adj, mask) + x = F.relu(self.embed_block1(x, adj, mask)) + xs = [x.mean(dim=1)] + x, adj, _, _ = dense_diff_pool(x, adj, s, mask) + + for i, (embed_block, pool_block) in enumerate( + zip(self.embed_blocks, self.pool_blocks)): + s = pool_block(x, adj) + x = F.relu(embed_block(x, adj)) + xs.append(x.mean(dim=1)) + if i < len(self.embed_blocks) - 1: + x, adj, _, _ = dense_diff_pool(x, adj, s) + + x = self.jump(xs) + x = F.relu(self.lin1(x)) + x = F.dropout(x, p=0.5, training=self.training) + x = self.lin2(x) + return F.log_softmax(x, dim=-1) + + def __repr__(self): + return self.__class__.__name__ diff --git a/external_experiments/sup_baseline/edge_pool.py b/external_experiments/sup_baseline/edge_pool.py new file mode 100644 index 0000000..a87d569 --- /dev/null +++ b/external_experiments/sup_baseline/edge_pool.py @@ -0,0 +1,50 @@ +import torch +import torch.nn.functional as F +from torch.nn import Linear +from torch_geometric.nn import (GraphConv, EdgePooling, global_mean_pool, + JumpingKnowledge) + + +class EdgePool(torch.nn.Module): + def __init__(self, dataset, num_layers, hidden): + super().__init__() + self.conv1 = GraphConv(dataset.num_features, hidden, aggr='mean') + self.convs = torch.nn.ModuleList() + self.pools = torch.nn.ModuleList() + self.convs.extend([ + GraphConv(hidden, hidden, aggr='mean') + for i in range(num_layers - 1) + ]) + self.pools.extend( + [EdgePooling(hidden) for i in range((num_layers) // 2)]) + self.jump = JumpingKnowledge(mode='cat') + self.lin1 = Linear(num_layers * hidden, hidden) + self.lin2 = Linear(hidden, dataset.num_classes) + + def reset_parameters(self): + self.conv1.reset_parameters() + for conv in self.convs: + conv.reset_parameters() + for pool in self.pools: + pool.reset_parameters() + self.lin1.reset_parameters() + self.lin2.reset_parameters() + + def forward(self, data): + x, edge_index, batch = data.x, data.edge_index, data.batch + x = F.relu(self.conv1(x, edge_index)) + xs = [global_mean_pool(x, batch)] + for i, conv in enumerate(self.convs): + x = F.relu(conv(x, edge_index)) + xs += [global_mean_pool(x, batch)] + if i % 2 == 0 and i < len(self.convs) - 1: + pool = self.pools[i // 2] + x, edge_index, batch, _ = pool(x, edge_index, batch=batch) + x = self.jump(xs) + x = F.relu(self.lin1(x)) + x = F.dropout(x, p=0.5, training=self.training) + x = self.lin2(x) + return F.log_softmax(x, dim=-1) + + def __repr__(self): + return self.__class__.__name__ diff --git a/external_experiments/sup_baseline/gcn.py b/external_experiments/sup_baseline/gcn.py new file mode 100644 index 0000000..1c6d079 --- /dev/null +++ b/external_experiments/sup_baseline/gcn.py @@ -0,0 +1,76 @@ +import torch +import torch.nn.functional as F +from torch.nn import Linear +from torch_geometric.nn import GCNConv, global_mean_pool, JumpingKnowledge + + +class GCN(torch.nn.Module): + def __init__(self, dataset, num_layers, hidden): + super().__init__() + self.conv1 = GCNConv(dataset.num_features, hidden) + self.convs = torch.nn.ModuleList() + for i in range(num_layers - 1): + self.convs.append(GCNConv(hidden, hidden)) + self.lin1 = Linear(hidden, hidden) + self.lin2 = Linear(hidden, dataset.num_classes) + + def reset_parameters(self): + self.conv1.reset_parameters() + for conv in self.convs: + conv.reset_parameters() + self.lin1.reset_parameters() + self.lin2.reset_parameters() + + def forward(self, data): + x, edge_index, batch = data.x, data.edge_index, data.batch + x = F.relu(self.conv1(x, edge_index)) + for conv in self.convs: + x = F.relu(conv(x, edge_index)) + x = global_mean_pool(x, batch) + x = F.relu(self.lin1(x)) + x = F.dropout(x, p=0.5, training=self.training) + x = self.lin2(x) + return F.log_softmax(x, dim=-1) + + def __repr__(self): + return self.__class__.__name__ + + +class GCNWithJK(torch.nn.Module): + def __init__(self, dataset, num_layers, hidden, mode='cat'): + super().__init__() + self.conv1 = GCNConv(dataset.num_features, hidden) + self.convs = torch.nn.ModuleList() + for i in range(num_layers - 1): + self.convs.append(GCNConv(hidden, hidden)) + self.jump = JumpingKnowledge(mode) + if mode == 'cat': + self.lin1 = Linear(num_layers * hidden, hidden) + else: + self.lin1 = Linear(hidden, hidden) + self.lin2 = Linear(hidden, dataset.num_classes) + + def reset_parameters(self): + self.conv1.reset_parameters() + for conv in self.convs: + conv.reset_parameters() + self.jump.reset_parameters() + self.lin1.reset_parameters() + self.lin2.reset_parameters() + + def forward(self, data): + x, edge_index, batch = data.x, data.edge_index, data.batch + x = F.relu(self.conv1(x, edge_index)) + xs = [x] + for conv in self.convs: + x = F.relu(conv(x, edge_index)) + xs += [x] + x = self.jump(xs) + x = global_mean_pool(x, batch) + x = F.relu(self.lin1(x)) + x = F.dropout(x, p=0.5, training=self.training) + x = self.lin2(x) + return F.log_softmax(x, dim=-1) + + def __repr__(self): + return self.__class__.__name__ diff --git a/external_experiments/sup_baseline/gin.py b/external_experiments/sup_baseline/gin.py new file mode 100644 index 0000000..3b3a810 --- /dev/null +++ b/external_experiments/sup_baseline/gin.py @@ -0,0 +1,208 @@ +import torch +import torch.nn.functional as F +from torch.nn import Linear, Sequential, ReLU, BatchNorm1d as BN +from torch_geometric.nn import GINConv, global_mean_pool, JumpingKnowledge + + +class GIN0(torch.nn.Module): + def __init__(self, dataset, num_layers, hidden): + super().__init__() + self.conv1 = GINConv( + Sequential( + Linear(dataset.num_features, hidden), + ReLU(), + Linear(hidden, hidden), + ReLU(), + BN(hidden), + ), train_eps=False) + self.convs = torch.nn.ModuleList() + for i in range(num_layers - 1): + self.convs.append( + GINConv( + Sequential( + Linear(hidden, hidden), + ReLU(), + Linear(hidden, hidden), + ReLU(), + BN(hidden), + ), train_eps=False)) + self.lin1 = Linear(hidden, hidden) + self.lin2 = Linear(hidden, dataset.num_classes) + + def reset_parameters(self): + self.conv1.reset_parameters() + for conv in self.convs: + conv.reset_parameters() + self.lin1.reset_parameters() + self.lin2.reset_parameters() + + def forward(self, data): + x, edge_index, batch = data.x, data.edge_index, data.batch + x = self.conv1(x, edge_index) + for conv in self.convs: + x = conv(x, edge_index) + x = global_mean_pool(x, batch) + x = F.relu(self.lin1(x)) + x = F.dropout(x, p=0.5, training=self.training) + x = self.lin2(x) + return F.log_softmax(x, dim=-1) + + def __repr__(self): + return self.__class__.__name__ + + +class GIN0WithJK(torch.nn.Module): + def __init__(self, dataset, num_layers, hidden, mode='cat'): + super().__init__() + self.conv1 = GINConv( + Sequential( + Linear(dataset.num_features, hidden), + ReLU(), + Linear(hidden, hidden), + ReLU(), + BN(hidden), + ), train_eps=False) + self.convs = torch.nn.ModuleList() + for i in range(num_layers - 1): + self.convs.append( + GINConv( + Sequential( + Linear(hidden, hidden), + ReLU(), + Linear(hidden, hidden), + ReLU(), + BN(hidden), + ), train_eps=False)) + self.jump = JumpingKnowledge(mode) + if mode == 'cat': + self.lin1 = Linear(num_layers * hidden, hidden) + else: + self.lin1 = Linear(hidden, hidden) + self.lin2 = Linear(hidden, dataset.num_classes) + + def reset_parameters(self): + self.conv1.reset_parameters() + for conv in self.convs: + conv.reset_parameters() + self.jump.reset_parameters() + self.lin1.reset_parameters() + self.lin2.reset_parameters() + + def forward(self, data): + x, edge_index, batch = data.x, data.edge_index, data.batch + x = self.conv1(x, edge_index) + xs = [x] + for conv in self.convs: + x = conv(x, edge_index) + xs += [x] + x = self.jump(xs) + x = global_mean_pool(x, batch) + x = F.relu(self.lin1(x)) + x = F.dropout(x, p=0.5, training=self.training) + x = self.lin2(x) + return F.log_softmax(x, dim=-1) + + def __repr__(self): + return self.__class__.__name__ + + +class GIN(torch.nn.Module): + def __init__(self, dataset, num_layers, hidden): + super().__init__() + self.conv1 = GINConv( + Sequential( + Linear(dataset.num_features, hidden), + ReLU(), + Linear(hidden, hidden), + ReLU(), + BN(hidden), + ), train_eps=True) + self.convs = torch.nn.ModuleList() + for i in range(num_layers - 1): + self.convs.append( + GINConv( + Sequential( + Linear(hidden, hidden), + ReLU(), + Linear(hidden, hidden), + ReLU(), + BN(hidden), + ), train_eps=True)) + self.lin1 = Linear(hidden, hidden) + self.lin2 = Linear(hidden, dataset.num_classes) + + def reset_parameters(self): + self.conv1.reset_parameters() + for conv in self.convs: + conv.reset_parameters() + self.lin1.reset_parameters() + self.lin2.reset_parameters() + + def forward(self, data): + x, edge_index, batch = data.x, data.edge_index, data.batch + x = self.conv1(x, edge_index) + for conv in self.convs: + x = conv(x, edge_index) + x = global_mean_pool(x, batch) + x = F.relu(self.lin1(x)) + x = F.dropout(x, p=0.5, training=self.training) + x = self.lin2(x) + return F.log_softmax(x, dim=-1) + + def __repr__(self): + return self.__class__.__name__ + + +class GINWithJK(torch.nn.Module): + def __init__(self, dataset, num_layers, hidden, mode='cat'): + super().__init__() + self.conv1 = GINConv( + Sequential( + Linear(dataset.num_features, hidden), + ReLU(), + Linear(hidden, hidden), + ReLU(), + BN(hidden), + ), train_eps=True) + self.convs = torch.nn.ModuleList() + for i in range(num_layers - 1): + self.convs.append( + GINConv( + Sequential( + Linear(hidden, hidden), + ReLU(), + Linear(hidden, hidden), + ReLU(), + BN(hidden), + ), train_eps=True)) + self.jump = JumpingKnowledge(mode) + if mode == 'cat': + self.lin1 = Linear(num_layers * hidden, hidden) + else: + self.lin1 = Linear(hidden, hidden) + self.lin2 = Linear(hidden, dataset.num_classes) + + def reset_parameters(self): + self.conv1.reset_parameters() + for conv in self.convs: + conv.reset_parameters() + self.jump.reset_parameters() + self.lin1.reset_parameters() + self.lin2.reset_parameters() + + def forward(self, data): + x, edge_index, batch = data.x, data.edge_index, data.batch + x = self.conv1(x, edge_index) + xs = [x] + for conv in self.convs: + x = conv(x, edge_index) + xs += [x] + x = self.jump(xs) + x = global_mean_pool(x, batch) + x = F.relu(self.lin1(x)) + x = F.dropout(x, p=0.5, training=self.training) + x = self.lin2(x) + return F.log_softmax(x, dim=-1) + + def __repr__(self): + return self.__class__.__name__ diff --git a/external_experiments/sup_baseline/global_attention.py b/external_experiments/sup_baseline/global_attention.py new file mode 100644 index 0000000..59341de --- /dev/null +++ b/external_experiments/sup_baseline/global_attention.py @@ -0,0 +1,38 @@ +import torch +import torch.nn.functional as F +from torch.nn import Linear +from torch_geometric.nn import SAGEConv, GlobalAttention + + +class GlobalAttentionNet(torch.nn.Module): + def __init__(self, dataset, num_layers, hidden): + super().__init__() + self.conv1 = SAGEConv(dataset.num_features, hidden) + self.convs = torch.nn.ModuleList() + for i in range(num_layers - 1): + self.convs.append(SAGEConv(hidden, hidden)) + self.att = GlobalAttention(Linear(hidden, 1)) + self.lin1 = Linear(hidden, hidden) + self.lin2 = Linear(hidden, dataset.num_classes) + + def reset_parameters(self): + self.conv1.reset_parameters() + for conv in self.convs: + conv.reset_parameters() + self.att.reset_parameters() + self.lin1.reset_parameters() + self.lin2.reset_parameters() + + def forward(self, data): + x, edge_index, batch = data.x, data.edge_index, data.batch + x = F.relu(self.conv1(x, edge_index)) + for conv in self.convs: + x = F.relu(conv(x, edge_index)) + x = self.att(x, batch) + x = F.relu(self.lin1(x)) + x = F.dropout(x, p=0.5, training=self.training) + x = self.lin2(x) + return F.log_softmax(x, dim=-1) + + def __repr__(self): + return self.__class__.__name__ diff --git a/external_experiments/sup_baseline/graclus.py b/external_experiments/sup_baseline/graclus.py new file mode 100644 index 0000000..ad37b4b --- /dev/null +++ b/external_experiments/sup_baseline/graclus.py @@ -0,0 +1,47 @@ +import torch +import torch.nn.functional as F +from torch.nn import Linear +from torch_geometric.data import Batch +from torch_geometric.nn import (GraphConv, graclus, max_pool, global_mean_pool, + JumpingKnowledge) + + +class Graclus(torch.nn.Module): + def __init__(self, dataset, num_layers, hidden): + super().__init__() + self.conv1 = GraphConv(dataset.num_features, hidden, aggr='mean') + self.convs = torch.nn.ModuleList() + for i in range(num_layers - 1): + self.convs.append(GraphConv(hidden, hidden, aggr='mean')) + self.jump = JumpingKnowledge(mode='cat') + self.lin1 = Linear(num_layers * hidden, hidden) + self.lin2 = Linear(hidden, dataset.num_classes) + + def reset_parameters(self): + self.conv1.reset_parameters() + for conv in self.convs: + conv.reset_parameters() + self.jump.reset_parameters() + self.lin1.reset_parameters() + self.lin2.reset_parameters() + + def forward(self, data): + x, edge_index, batch = data.x, data.edge_index, data.batch + x = F.relu(self.conv1(x, edge_index)) + xs = [global_mean_pool(x, batch)] + for i, conv in enumerate(self.convs): + x = F.relu(conv(x, edge_index)) + xs += [global_mean_pool(x, batch)] + if i % 2 == 0 and i < len(self.convs) - 1: + cluster = graclus(edge_index, num_nodes=x.size(0)) + data = Batch(x=x, edge_index=edge_index, batch=batch) + data = max_pool(cluster, data) + x, edge_index, batch = data.x, data.edge_index, data.batch + x = self.jump(xs) + x = F.relu(self.lin1(x)) + x = F.dropout(x, p=0.5, training=self.training) + x = self.lin2(x) + return F.log_softmax(x, dim=-1) + + def __repr__(self): + return self.__class__.__name__ diff --git a/external_experiments/sup_baseline/graph_sage.py b/external_experiments/sup_baseline/graph_sage.py new file mode 100644 index 0000000..65f60f7 --- /dev/null +++ b/external_experiments/sup_baseline/graph_sage.py @@ -0,0 +1,76 @@ +import torch +import torch.nn.functional as F +from torch.nn import Linear +from torch_geometric.nn import SAGEConv, global_mean_pool, JumpingKnowledge + + +class GraphSAGE(torch.nn.Module): + def __init__(self, dataset, num_layers, hidden): + super().__init__() + self.conv1 = SAGEConv(dataset.num_features, hidden) + self.convs = torch.nn.ModuleList() + for i in range(num_layers - 1): + self.convs.append(SAGEConv(hidden, hidden)) + self.lin1 = Linear(hidden, hidden) + self.lin2 = Linear(hidden, dataset.num_classes) + + def reset_parameters(self): + self.conv1.reset_parameters() + for conv in self.convs: + conv.reset_parameters() + self.lin1.reset_parameters() + self.lin2.reset_parameters() + + def forward(self, data): + x, edge_index, batch = data.x, data.edge_index, data.batch + x = F.relu(self.conv1(x, edge_index)) + for conv in self.convs: + x = F.relu(conv(x, edge_index)) + x = global_mean_pool(x, batch) + x = F.relu(self.lin1(x)) + x = F.dropout(x, p=0.5, training=self.training) + x = self.lin2(x) + return F.log_softmax(x, dim=-1) + + def __repr__(self): + return self.__class__.__name__ + + +class GraphSAGEWithJK(torch.nn.Module): + def __init__(self, dataset, num_layers, hidden, mode='cat'): + super().__init__() + self.conv1 = SAGEConv(dataset.num_features, hidden) + self.convs = torch.nn.ModuleList() + for i in range(num_layers - 1): + self.convs.append(SAGEConv(hidden, hidden)) + self.jump = JumpingKnowledge(mode) + if mode == 'cat': + self.lin1 = Linear(num_layers * hidden, hidden) + else: + self.lin1 = Linear(hidden, hidden) + self.lin2 = Linear(hidden, dataset.num_classes) + + def reset_parameters(self): + self.conv1.reset_parameters() + for conv in self.convs: + conv.reset_parameters() + self.jump.reset_parameters() + self.lin1.reset_parameters() + self.lin2.reset_parameters() + + def forward(self, data): + x, edge_index, batch = data.x, data.edge_index, data.batch + x = F.relu(self.conv1(x, edge_index)) + xs = [x] + for conv in self.convs: + x = F.relu(conv(x, edge_index)) + xs += [x] + x = self.jump(xs) + x = global_mean_pool(x, batch) + x = F.relu(self.lin1(x)) + x = F.dropout(x, p=0.5, training=self.training) + x = self.lin2(x) + return F.log_softmax(x, dim=-1) + + def __repr__(self): + return self.__class__.__name__ diff --git a/external_experiments/sup_baseline/main.py b/external_experiments/sup_baseline/main.py new file mode 100644 index 0000000..9539050 --- /dev/null +++ b/external_experiments/sup_baseline/main.py @@ -0,0 +1,72 @@ +from itertools import product + +import argparse +from datasets import get_dataset +from train_eval import cross_validation_with_val_set + +from gcn import GCN, GCNWithJK +from graph_sage import GraphSAGE, GraphSAGEWithJK +from gin import GIN0, GIN0WithJK, GIN, GINWithJK +from graclus import Graclus +from top_k import TopK +from sag_pool import SAGPool +from diff_pool import DiffPool +from edge_pool import EdgePool +from global_attention import GlobalAttentionNet +from set2set import Set2SetNet +from sort_pool import SortPool +from asap import ASAP + +parser = argparse.ArgumentParser() +parser.add_argument('--epochs', type=int, default=100) +parser.add_argument('--batch_size', type=int, default=128) +parser.add_argument('--lr', type=float, default=0.01) +parser.add_argument('--lr_decay_factor', type=float, default=0.5) +parser.add_argument('--lr_decay_step_size', type=int, default=50) +args = parser.parse_args() + +layers = [1, 2, 3, 4, 5] +hiddens = [64, 128] +datasets = ["PROTEINS", 'IMDB-MULTI', 'DD', 'MUTAG', 'PinWheels', 'SimpleTwoCycles'] +nets = [ + GCN, + GraphSAGE, + GIN0, + GIN, +] + + +def logger(info): + fold, epoch = info['fold'] + 1, info['epoch'] + val_loss, test_acc = info['val_loss'], info['test_acc'] + print(f'{fold:02d}/{epoch:03d}: Val Loss: {val_loss:.4f}, ' + f'Test Accuracy: {test_acc:.3f}') + + +results = [] +for dataset_name, Net in product(datasets, nets): + best_result = (float('inf'), 0, 0) # (loss, acc, std) + print(f'--\n{dataset_name} - {Net.__name__}') + for num_layers, hidden in product(layers, hiddens): + dataset = get_dataset(dataset_name, sparse=Net != DiffPool) + model = Net(dataset, num_layers, hidden) + loss, acc, std = cross_validation_with_val_set( + dataset, + model, + folds=10, + epochs=args.epochs, + batch_size=args.batch_size, + lr=args.lr, + lr_decay_factor=args.lr_decay_factor, + lr_decay_step_size=args.lr_decay_step_size, + weight_decay=0, + logger=None, + ) + if loss < best_result[0]: + best_result = (loss, acc, std) + + desc = f'{best_result[1]:.3f} ± {best_result[2]:.3f}' + print(f'Best result - {desc}') + results += [f'{dataset_name} - {model}: {desc}'] +results = '\n'.join(results) +print(f'--\n{results}') diff --git a/external_experiments/sup_baseline/main_performance.py b/external_experiments/sup_baseline/main_performance.py new file mode 100644 index 0000000..ec9c6b4 --- /dev/null +++ b/external_experiments/sup_baseline/main_performance.py @@ -0,0 +1,78 @@ +import argparse +from itertools import product + +import torch + +from torch_geometric import seed_everything +from torch_geometric.loader import DataLoader +from torch_geometric.profile import profileit, timeit, get_stats_summary + +from datasets import get_dataset +from train_eval import train, eval_acc + +from gcn import GCN +from graph_sage import GraphSAGE +from gin import GIN + +seed_everything(0) + +parser = argparse.ArgumentParser() +parser.add_argument('--epochs', type=int, default=100) +parser.add_argument('--batch_size', type=int, default=128) +parser.add_argument('--lr', type=float, default=0.01) +parser.add_argument('--warmup_profile', type=int, default=1, + help='Skip the first few runs') +parser.add_argument('--goal_accuracy', type=int, default=1, + help='The goal test accuracy') +args = parser.parse_args() + +layers = [1, 2, 3] +hiddens = [16, 32] + +datasets = ['MUTAG', 'PROTEINS', 'IMDB-BINARY', 'REDDIT-BINARY'] + +nets = [ + GCN, + GraphSAGE, + GIN, +] + +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + +# Decorate train and eval functions: +train = profileit(print_layer_stats=False)(train) +eval_acc = timeit()(eval_acc) + +for dataset_name, Net in product(datasets, nets): + dataset = get_dataset(dataset_name, sparse=True) + num_train = int(len(dataset) * 0.8) + num_val = int(len(dataset) * 0.1) + + train_dataset = dataset[:num_train] + val_dataset = dataset[num_train:num_train + num_val] + test_dataset = dataset[num_train + num_val:] + + train_loader = DataLoader(train_dataset, batch_size=args.batch_size, + shuffle=True) + val_loader = DataLoader(val_dataset, batch_size=args.batch_size, + shuffle=False) + test_loader = DataLoader(test_dataset, batch_size=args.batch_size, + shuffle=False) + + for num_layers, hidden in product(layers, hiddens): + print(f'--\n{dataset_name} - {Net.__name__} - {num_layers} - {hidden}') + + model = Net(dataset, num_layers, hidden).to(device) + optimizer = torch.optim.Adam(model.parameters(), lr=args.lr) + + stats_list = [] + for epoch in range(1, args.epochs + 1): + loss, stats = train(model, optimizer, train_loader) + val_acc, val_time = eval_acc(model, val_loader) + test_acc, test_time = eval_acc(model, test_loader) + + if epoch >= args.warmup_profile: + stats_list.append(stats) + + stats_summary = get_stats_summary(stats_list) + print(stats_summary) diff --git a/external_experiments/sup_baseline/run_sup_baseline.sh b/external_experiments/sup_baseline/run_sup_baseline.sh new file mode 100644 index 0000000..052edb1 --- /dev/null +++ b/external_experiments/sup_baseline/run_sup_baseline.sh @@ -0,0 +1,3 @@ +#!/bin/bash + +python3 main.py \ No newline at end of file diff --git a/external_experiments/sup_baseline/sag_pool.py b/external_experiments/sup_baseline/sag_pool.py new file mode 100644 index 0000000..ff36007 --- /dev/null +++ b/external_experiments/sup_baseline/sag_pool.py @@ -0,0 +1,51 @@ +import torch +import torch.nn.functional as F +from torch.nn import Linear +from torch_geometric.nn import (GraphConv, SAGPooling, global_mean_pool, + JumpingKnowledge) + + +class SAGPool(torch.nn.Module): + def __init__(self, dataset, num_layers, hidden, ratio=0.8): + super().__init__() + self.conv1 = GraphConv(dataset.num_features, hidden, aggr='mean') + self.convs = torch.nn.ModuleList() + self.pools = torch.nn.ModuleList() + self.convs.extend([ + GraphConv(hidden, hidden, aggr='mean') + for i in range(num_layers - 1) + ]) + self.pools.extend( + [SAGPooling(hidden, ratio) for i in range((num_layers) // 2)]) + self.jump = JumpingKnowledge(mode='cat') + self.lin1 = Linear(num_layers * hidden, hidden) + self.lin2 = Linear(hidden, dataset.num_classes) + + def reset_parameters(self): + self.conv1.reset_parameters() + for conv in self.convs: + conv.reset_parameters() + for pool in self.pools: + pool.reset_parameters() + self.lin1.reset_parameters() + self.lin2.reset_parameters() + + def forward(self, data): + x, edge_index, batch = data.x, data.edge_index, data.batch + x = F.relu(self.conv1(x, edge_index)) + xs = [global_mean_pool(x, batch)] + for i, conv in enumerate(self.convs): + x = F.relu(conv(x, edge_index)) + xs += [global_mean_pool(x, batch)] + if i % 2 == 0 and i < len(self.convs) - 1: + pool = self.pools[i // 2] + x, edge_index, _, batch, _, _ = pool(x, edge_index, + batch=batch) + x = self.jump(xs) + x = F.relu(self.lin1(x)) + x = F.dropout(x, p=0.5, training=self.training) + x = self.lin2(x) + return F.log_softmax(x, dim=-1) + + def __repr__(self): + return self.__class__.__name__ diff --git a/external_experiments/sup_baseline/set2set.py b/external_experiments/sup_baseline/set2set.py new file mode 100644 index 0000000..24950ec --- /dev/null +++ b/external_experiments/sup_baseline/set2set.py @@ -0,0 +1,38 @@ +import torch +import torch.nn.functional as F +from torch.nn import Linear +from torch_geometric.nn import SAGEConv, Set2Set + + +class Set2SetNet(torch.nn.Module): + def __init__(self, dataset, num_layers, hidden): + super().__init__() + self.conv1 = SAGEConv(dataset.num_features, hidden) + self.convs = torch.nn.ModuleList() + for i in range(num_layers - 1): + self.convs.append(SAGEConv(hidden, hidden)) + self.set2set = Set2Set(hidden, processing_steps=4) + self.lin1 = Linear(2 * hidden, hidden) + self.lin2 = Linear(hidden, dataset.num_classes) + + def reset_parameters(self): + self.conv1.reset_parameters() + for conv in self.convs: + conv.reset_parameters() + self.set2set.reset_parameters() + self.lin1.reset_parameters() + self.lin2.reset_parameters() + + def forward(self, data): + x, edge_index, batch = data.x, data.edge_index, data.batch + x = F.relu(self.conv1(x, edge_index)) + for conv in self.convs: + x = F.relu(conv(x, edge_index)) + x = self.set2set(x, batch) + x = F.relu(self.lin1(x)) + x = F.dropout(x, p=0.5, training=self.training) + x = self.lin2(x) + return F.log_softmax(x, dim=-1) + + def __repr__(self): + return self.__class__.__name__ diff --git a/external_experiments/sup_baseline/sort_pool.py b/external_experiments/sup_baseline/sort_pool.py new file mode 100644 index 0000000..17153c2 --- /dev/null +++ b/external_experiments/sup_baseline/sort_pool.py @@ -0,0 +1,42 @@ +import torch +import torch.nn.functional as F +from torch.nn import Linear, Conv1d +from torch_geometric.nn import SAGEConv, global_sort_pool + + +class SortPool(torch.nn.Module): + def __init__(self, dataset, num_layers, hidden): + super().__init__() + self.k = 30 + self.conv1 = SAGEConv(dataset.num_features, hidden) + self.convs = torch.nn.ModuleList() + for i in range(num_layers - 1): + self.convs.append(SAGEConv(hidden, hidden)) + self.conv1d = Conv1d(hidden, 32, 5) + self.lin1 = Linear(32 * (self.k - 5 + 1), hidden) + self.lin2 = Linear(hidden, dataset.num_classes) + + def reset_parameters(self): + self.conv1.reset_parameters() + for conv in self.convs: + conv.reset_parameters() + self.conv1d.reset_parameters() + self.lin1.reset_parameters() + self.lin2.reset_parameters() + + def forward(self, data): + x, edge_index, batch = data.x, data.edge_index, data.batch + x = F.relu(self.conv1(x, edge_index)) + for conv in self.convs: + x = F.relu(conv(x, edge_index)) + x = global_sort_pool(x, batch, self.k) + x = x.view(len(x), self.k, -1).permute(0, 2, 1) + x = F.relu(self.conv1d(x)) + x = x.view(len(x), -1) + x = F.relu(self.lin1(x)) + x = F.dropout(x, p=0.5, training=self.training) + x = self.lin2(x) + return F.log_softmax(x, dim=-1) + + def __repr__(self): + return self.__class__.__name__ diff --git a/external_experiments/sup_baseline/statistics.py b/external_experiments/sup_baseline/statistics.py new file mode 100644 index 0000000..fd3491a --- /dev/null +++ b/external_experiments/sup_baseline/statistics.py @@ -0,0 +1,20 @@ +from kernel.datasets import get_dataset + + +def print_dataset(dataset): + num_nodes = num_edges = 0 + for data in dataset: + num_nodes += data.num_nodes + num_edges += data.num_edges + + print('Name', dataset) + print('Graphs', len(dataset)) + print('Nodes', num_nodes / len(dataset)) + print('Edges', (num_edges // 2) / len(dataset)) + print('Features', dataset.num_features) + print('Classes', dataset.num_classes) + print() + + +for name in ['MUTAG', 'PROTEINS', 'COLLAB', 'IMDB-BINARY', 'REDDIT-BINARY']: + print_dataset(get_dataset(name)) diff --git a/external_experiments/sup_baseline/top_k.py b/external_experiments/sup_baseline/top_k.py new file mode 100644 index 0000000..45e1abe --- /dev/null +++ b/external_experiments/sup_baseline/top_k.py @@ -0,0 +1,51 @@ +import torch +import torch.nn.functional as F +from torch.nn import Linear +from torch_geometric.nn import (GraphConv, TopKPooling, global_mean_pool, + JumpingKnowledge) + + +class TopK(torch.nn.Module): + def __init__(self, dataset, num_layers, hidden, ratio=0.8): + super().__init__() + self.conv1 = GraphConv(dataset.num_features, hidden, aggr='mean') + self.convs = torch.nn.ModuleList() + self.pools = torch.nn.ModuleList() + self.convs.extend([ + GraphConv(hidden, hidden, aggr='mean') + for i in range(num_layers - 1) + ]) + self.pools.extend( + [TopKPooling(hidden, ratio) for i in range((num_layers) // 2)]) + self.jump = JumpingKnowledge(mode='cat') + self.lin1 = Linear(num_layers * hidden, hidden) + self.lin2 = Linear(hidden, dataset.num_classes) + + def reset_parameters(self): + self.conv1.reset_parameters() + for conv in self.convs: + conv.reset_parameters() + for pool in self.pools: + pool.reset_parameters() + self.lin1.reset_parameters() + self.lin2.reset_parameters() + + def forward(self, data): + x, edge_index, batch = data.x, data.edge_index, data.batch + x = F.relu(self.conv1(x, edge_index)) + xs = [global_mean_pool(x, batch)] + for i, conv in enumerate(self.convs): + x = F.relu(conv(x, edge_index)) + xs += [global_mean_pool(x, batch)] + if i % 2 == 0 and i < len(self.convs) - 1: + pool = self.pools[i // 2] + x, edge_index, _, batch, _, _ = pool(x, edge_index, + batch=batch) + x = self.jump(xs) + x = F.relu(self.lin1(x)) + x = F.dropout(x, p=0.5, training=self.training) + x = self.lin2(x) + return F.log_softmax(x, dim=-1) + + def __repr__(self): + return self.__class__.__name__ diff --git a/external_experiments/sup_baseline/train_eval.py b/external_experiments/sup_baseline/train_eval.py new file mode 100644 index 0000000..9a8e16b --- /dev/null +++ b/external_experiments/sup_baseline/train_eval.py @@ -0,0 +1,145 @@ +import time +import sys + +sys.path.append("/home/zhang.680/github/NEURALNETWORKS/GNNS/pytorch_geometric/") +import torch +import torch.nn.functional as F +from torch import tensor +from torch.optim import Adam +from sklearn.model_selection import StratifiedKFold +# from torch_geometric.loader import DataLoader, DenseDataLoader as DenseLoader +from torch_geometric.data import DataLoader +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + +def cross_validation_with_val_set(dataset, model, folds, epochs, batch_size, + lr, lr_decay_factor, lr_decay_step_size, + weight_decay, logger=None): + + val_losses, accs, durations = [], [], [] + for fold, (train_idx, test_idx, + val_idx) in enumerate(zip(*k_fold(dataset, folds))): + + train_dataset = dataset[train_idx] + test_dataset = dataset[test_idx] + val_dataset = dataset[val_idx] + + if 'adj' in train_dataset[0]: + train_loader = DenseLoader(train_dataset, batch_size, shuffle=True) + val_loader = DenseLoader(val_dataset, batch_size, shuffle=False) + test_loader = DenseLoader(test_dataset, batch_size, shuffle=False) + else: + train_loader = DataLoader(train_dataset, batch_size, shuffle=True) + val_loader = DataLoader(val_dataset, batch_size, shuffle=False) + test_loader = DataLoader(test_dataset, batch_size, shuffle=False) + + model.to(device).reset_parameters() + optimizer = Adam(model.parameters(), lr=lr, weight_decay=weight_decay) + + if torch.cuda.is_available(): + torch.cuda.synchronize() + + t_start = time.perf_counter() + + for epoch in range(1, epochs + 1): + train_loss = train(model, optimizer, train_loader) + val_losses.append(eval_loss(model, val_loader)) + accs.append(eval_acc(model, test_loader)) + eval_info = { + 'fold': fold, + 'epoch': epoch, + 'train_loss': train_loss, + 'val_loss': val_losses[-1], + 'test_acc': accs[-1], + } + + if logger is not None: + logger(eval_info) + + if epoch % lr_decay_step_size == 0: + for param_group in optimizer.param_groups: + param_group['lr'] = lr_decay_factor * param_group['lr'] + + if torch.cuda.is_available(): + torch.cuda.synchronize() + + t_end = time.perf_counter() + durations.append(t_end - t_start) + + loss, acc, duration = tensor(val_losses), tensor(accs), tensor(durations) + loss, acc = loss.view(folds, epochs), acc.view(folds, epochs) + loss, argmin = loss.min(dim=1) + acc = acc[torch.arange(folds, dtype=torch.long), argmin] + + loss_mean = loss.mean().item() + acc_mean = acc.mean().item() + acc_std = acc.std().item() + duration_mean = duration.mean().item() + print(f'Val Loss: {loss_mean:.4f}, Test Accuracy: {acc_mean:.3f} ' + f'± {acc_std:.3f}, Duration: {duration_mean:.3f}') + + return loss_mean, acc_mean, acc_std + + +def k_fold(dataset, folds): + skf = StratifiedKFold(folds, shuffle=True, random_state=12345) + + test_indices, train_indices = [], [] + for _, idx in skf.split(torch.zeros(len(dataset)), dataset.data.y): + test_indices.append(torch.from_numpy(idx).to(torch.long)) + + val_indices = [test_indices[i - 1] for i in range(folds)] + + for i in range(folds): + train_mask = torch.ones(len(dataset), dtype=torch.bool) + train_mask[test_indices[i]] = 0 + train_mask[val_indices[i]] = 0 + train_indices.append(train_mask.nonzero(as_tuple=False).view(-1)) + + return train_indices, test_indices, val_indices + + +def num_graphs(data): + if hasattr(data, 'num_graphs'): + return data.num_graphs + else: + return data.x.size(0) + + +def train(model, optimizer, loader): + model.train() + + total_loss = 0 + for data in loader: + optimizer.zero_grad() + data = data.to(device) + out = model(data) + loss = F.nll_loss(out.float(), data.y.view(-1).long()) + loss.backward() + total_loss += loss.item() * num_graphs(data) + optimizer.step() + return total_loss / len(loader.dataset) + + +def eval_acc(model, loader): + model.eval() + + correct = 0 + for data in loader: + data = data.to(device) + with torch.no_grad(): + pred = model(data).max(1)[1] + correct += pred.eq(data.y.view(-1)).sum().item() + return correct / len(loader.dataset) + + +def eval_loss(model, loader): + model.eval() + + loss = 0 + for data in loader: + data = data.to(device) + with torch.no_grad(): + out = model(data) + loss += F.nll_loss(out.float(), data.y.view(-1).long(), reduction='sum').item() + return loss / len(loader.dataset) diff --git a/gnn/__init__.py b/gnn/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/gnn/__pycache__/__init__.cpython-38.pyc b/gnn/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..944c548c77f341fa3d28fe5011b2aa8acc577dfa GIT binary patch literal 177 zcmWIL<>g`kg4An0Ng(<$h(HF6K#l_t7qb9~6oz01O-8?!3`HPe1o10OKO;XkRlhhh zH$P85J+maEG)dpjH8jZ4$Imq++&{=WSl`{x4@eXx7G$_ql%(dRq^7uK=9CmAmSpDV s`J^Tm!;`C>Bq-s=4F<|$LkeT-r}&y%}*)KNwovn_Zf&80Dn>}(f|Me literal 0 HcmV?d00001 diff --git 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+1,250 @@ +import torch +from torch_geometric.nn import MessagePassing +import torch.nn.functional as F +from torch_geometric.nn import global_mean_pool, global_add_pool +from ogb.graphproppred.mol_encoder import AtomEncoder,BondEncoder +from torch_geometric.utils import degree + +import math + +### GIN convolution along the graph structure +class GINConv(MessagePassing): + def __init__(self, emb_dim): + ''' + emb_dim (int): node embedding dimensionality + ''' + + super(GINConv, self).__init__(aggr = "add") + + self.mlp = torch.nn.Sequential(torch.nn.Linear(emb_dim, 2*emb_dim), torch.nn.BatchNorm1d(2*emb_dim), torch.nn.ReLU(), torch.nn.Linear(2*emb_dim, emb_dim)) + self.eps = torch.nn.Parameter(torch.Tensor([0])) + + self.bond_encoder = BondEncoder(emb_dim = emb_dim) + + def forward(self, x, edge_index, edge_attr): + if edge_attr is not None: + edge_embedding = self.bond_encoder(edge_attr) + else: + edge_embedding= None + out = self.mlp((1 + self.eps) *x + self.propagate(edge_index, x=x, edge_attr=edge_embedding)) + + return out + + def message(self, x_j, edge_attr): + if edge_attr is None: + return F.relu(x_j) + else: + return F.relu(x_j + edge_attr) + + def update(self, aggr_out): + return aggr_out + +### GCN convolution along the graph structure +class GCNConv(MessagePassing): + def __init__(self, emb_dim): + super(GCNConv, self).__init__(aggr='add') + + self.linear = torch.nn.Linear(emb_dim, emb_dim) + self.root_emb = torch.nn.Embedding(1, emb_dim) + self.bond_encoder = BondEncoder(emb_dim = emb_dim) + + def forward(self, x, edge_index, edge_attr): + x = self.linear(x) + edge_embedding = self.bond_encoder(edge_attr) + + row, col = edge_index + + #edge_weight = torch.ones((edge_index.size(1), ), device=edge_index.device) + deg = degree(row, x.size(0), dtype = x.dtype) + 1 + deg_inv_sqrt = deg.pow(-0.5) + deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0 + + norm = deg_inv_sqrt[row] * deg_inv_sqrt[col] + + return self.propagate(edge_index, x=x, edge_attr = edge_embedding, norm=norm) + F.relu(x + self.root_emb.weight) * 1./deg.view(-1,1) + + def message(self, x_j, edge_attr, norm): + return norm.view(-1, 1) * F.relu(x_j + edge_attr) + + def update(self, aggr_out): + return aggr_out + + +### GNN to generate node embedding +class GNN_node(torch.nn.Module): + """ + Output: + node representations + """ + def __init__(self, num_layer, emb_dim, drop_ratio = 0.5, JK = "filt", residual = False, gnn_type = 'gin'): + ''' + emb_dim (int): node embedding dimensionality + num_layer (int): number of GNN message passing layers + ''' + + super(GNN_node, self).__init__() + self.num_layer = num_layer + self.drop_ratio = drop_ratio + self.JK = JK + ### add residual connection or not + self.residual = residual + + if self.num_layer < 2: + raise ValueError("Number of GNN layers must be greater than 1.") + + self.atom_encoder = AtomEncoder(emb_dim) + + ###List of GNNs + self.convs = torch.nn.ModuleList() + self.batch_norms = torch.nn.ModuleList() + + for layer in range(num_layer): + if gnn_type == 'gin': + self.convs.append(GINConv(emb_dim)) + elif gnn_type == 'gcn': + self.convs.append(GCNConv(emb_dim)) + else: + raise ValueError('Undefined GNN type called {}'.format(gnn_type)) + + self.batch_norms.append(torch.nn.BatchNorm1d(emb_dim)) + + def forward(self, batched_data): + x, edge_index, edge_attr, batch = batched_data.x, batched_data.edge_index, batched_data.edge_attr, batched_data.batch + + ### computing input node embedding + + h_list = [self.atom_encoder(x)] + for layer in range(self.num_layer): + + h = self.convs[layer](h_list[layer], edge_index, edge_attr) + h = self.batch_norms[layer](h) + + if layer == self.num_layer - 1: + #remove relu for the last layer + h = F.dropout(h, self.drop_ratio, training = self.training) + else: + h = F.dropout(F.relu(h), self.drop_ratio, training = self.training) + + if self.residual: + h += h_list[layer] + + h_list.append(h) + + ### Different implementations of Jk-concat + if self.JK == "last": + node_representation = h_list[-1] + elif self.JK == "sum": + node_representation = 0 + for layer in range(self.num_layer + 1): + node_representation += h_list[layer] + elif self.JK=='filt': + x = torch.cat(h_list, dim=1) + node_representation= self.fc(x).squeeze() + return node_representation + + +### Virtual GNN to generate node embedding +class GNN_node_Virtualnode(torch.nn.Module): + """ + Output: + node representations + """ + def __init__(self, num_layer, emb_dim, drop_ratio = 0.5, JK = "last", residual = False, gnn_type = 'gin'): + ''' + emb_dim (int): node embedding dimensionality + ''' + + super(GNN_node_Virtualnode, self).__init__() + self.num_layer = num_layer + self.drop_ratio = drop_ratio + self.JK = JK + ### add residual connection or not + self.residual = residual + + if self.num_layer < 2: + raise ValueError("Number of GNN layers must be greater than 1.") + + self.atom_encoder = AtomEncoder(emb_dim) + + ### set the initial virtual node embedding to 0. + self.virtualnode_embedding = torch.nn.Embedding(1, emb_dim) + torch.nn.init.constant_(self.virtualnode_embedding.weight.data, 0) + + ### List of GNNs + self.convs = torch.nn.ModuleList() + ### batch norms applied to node embeddings + self.batch_norms = torch.nn.ModuleList() + + ### List of MLPs to transform virtual node at every layer + self.mlp_virtualnode_list = torch.nn.ModuleList() + + for layer in range(num_layer): + if gnn_type == 'gin': + self.convs.append(GINConv(emb_dim)) + elif gnn_type == 'gcn': + self.convs.append(GCNConv(emb_dim)) + else: + raise ValueError('Undefined GNN type called {}'.format(gnn_type)) + + self.batch_norms.append(torch.nn.BatchNorm1d(emb_dim)) + + for layer in range(num_layer - 1): + self.mlp_virtualnode_list.append(torch.nn.Sequential(torch.nn.Linear(emb_dim, 2*emb_dim), torch.nn.BatchNorm1d(2*emb_dim), torch.nn.ReLU(), \ + torch.nn.Linear(2*emb_dim, emb_dim), torch.nn.BatchNorm1d(emb_dim), torch.nn.ReLU())) + + + def forward(self, batched_data): + + #x, edge_index, edge_attr, batch = batched_data.x, batched_data.edge_index, batched_data.edge_attr, batched_data.batch + x, edge_index, edge_attr, batch, node_deg_emb= batched_data.x, batched_data.edge_index, batched_data.edge_attr, batched_data.batch, batched_data.node_deg + ### virtual node embeddings for graphs + virtualnode_embedding = self.virtualnode_embedding(torch.zeros(batch[-1].item() + 1).to(edge_index.dtype).to(edge_index.device)) + + + if node_deg_emb is not None: + h_list= [torch.cat([self.atom_encoder(x), node_deg_emb], dim= 1)] + else: + h_list = [self.atom_encoder(x)] + for layer in range(self.num_layer): + ### add message from virtual nodes to graph nodes + h_list[layer] = h_list[layer] + virtualnode_embedding[batch] + + ### Message passing among graph nodes + h = self.convs[layer](h_list[layer], edge_index, edge_attr) + + h = self.batch_norms[layer](h) + if layer == self.num_layer - 1: + #remove relu for the last layer + h = F.dropout(h, self.drop_ratio, training = self.training) + else: + h = F.dropout(F.relu(h), self.drop_ratio, training = self.training) + + if self.residual: + h = h + h_list[layer] + + h_list.append(h) + + ### update the virtual nodes + if layer < self.num_layer - 1: + ### add message from graph nodes to virtual nodes + virtualnode_embedding_temp = global_add_pool(h_list[layer], batch) + virtualnode_embedding + ### transform virtual nodes using MLP + + if self.residual: + virtualnode_embedding = virtualnode_embedding + F.dropout(self.mlp_virtualnode_list[layer](virtualnode_embedding_temp), self.drop_ratio, training = self.training) + else: + virtualnode_embedding = F.dropout(self.mlp_virtualnode_list[layer](virtualnode_embedding_temp), self.drop_ratio, training = self.training) + + ### Different implementations of Jk-concat + if self.JK == "last": + node_representation = h_list[-1] + elif self.JK == "sum": + node_representation = 0 + for layer in range(self.num_layer + 1): + node_representation += h_list[layer] + + return node_representation + + +if __name__ == "__main__": + pass \ No newline at end of file diff --git a/gnn/dd_model.py b/gnn/dd_model.py new file mode 100644 index 0000000..7b8a5e1 --- /dev/null +++ b/gnn/dd_model.py @@ -0,0 +1,925 @@ +import torch +import numpy as np +import torch.nn as nn + +import functools + +from torchPHext.torchex_PHext.nn import SLayerRationalHat, SLayerSquare, SLayerExponential +from torch_geometric.nn import SAGEConv, LEConv, GINConv, global_add_pool, global_sort_pool, global_mean_pool, \ + global_max_pool + +from torchPHext.torchex_PHext import pershom as pershom_ext +from torch_geometric.nn import GINConv, global_add_pool +import sys + +sys.path.append(".") + +ph_extended_link_tree = pershom_ext.pershom_backend.__C.VertExtendedFiltCompCuda_link_cut_tree__extended_persistence_batch + +def gin_mlp_factory(gin_mlp_type: str, dim_in: int, dim_out: int): + if gin_mlp_type == 'lin': + return nn.Linear(dim_in, dim_out) + + elif gin_mlp_type == 'lin_lrelu_lin': + return nn.Sequential( + nn.Linear(dim_in, dim_in), + nn.LeakyReLU(), + nn.Linear(dim_in, dim_out) + ) + + elif gin_mlp_type == 'lin_bn_lrelu_lin': + return nn.Sequential( + nn.Linear(dim_in, dim_in), + nn.BatchNorm1d(dim_in), + nn.LeakyReLU(), + nn.Linear(dim_in, dim_out) + ) + else: + raise ValueError("Unknown gin_mlp_type!") + + +def ClassifierHead( + dataset, + dim_in: int = None, + hidden_dim: int = None, + drop_out: float = None): + assert (0.0 <= drop_out) and (drop_out < 1.0) + assert dim_in is not None + assert drop_out is not None + assert hidden_dim is not None + + tmp = [ + nn.Linear(dim_in, hidden_dim), + nn.LeakyReLU(), + ] + + if drop_out > 0: + tmp += [nn.Dropout(p=drop_out)] + + tmp += [nn.Linear(hidden_dim, dataset.num_classes)] + + return nn.Sequential(*tmp) + +class DegreeOnlyFiltration(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, batch): + tmp = [] + for i, j in zip(batch.sample_pos[:-1], batch.sample_pos[1:]): + max_deg = batch.node_deg[i:j].max() + + t = torch.ones(j - i, dtype=torch.float, device=batch.node_deg.device) + t = t * max_deg + tmp.append(t) + + max_deg = torch.cat(tmp, dim=0) + + normalized_node_deg = batch.node_deg.float() / max_deg + + return normalized_node_deg + +class ClassicGNN(torch.nn.Module): + def __init__(self, + dataset, + use_node_degree=None, + set_node_degree_uninformative=None, + use_node_label=None, + use_raw_node_label=None, + gin_number=None, + gin_dimension=None, + conv_type='GIN', + gin_mlp_type=None, + cls_hidden_dimension=512, + drop_out=0.5, + **kwargs + ): + super().__init__() + + dim = gin_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if set_node_degree_uninformative and use_node_degree: + self.embed_deg = UniformativeDummyEmbedding(gin_dimension) + elif use_node_degree: + self.embed_deg = nn.Embedding(max_node_deg + 1, dim) + else: + self.embed_deg = None + + self.embed_lab = nn.Embedding(num_node_lab, dim) if use_node_label else None + self.use_raw_node_label = use_raw_node_label + + dim_input = dim*((self.embed_deg is not None) + (self.embed_lab is not None)) + #dim_input = dim * ((self.embed_deg is not None)) + if (use_node_label and use_raw_node_label): + dim_input += dataset[0].x.size(1) + + dims = [dim_input] + (gin_number) * [dim] + + self.convs = nn.ModuleList() + self.bns = nn.ModuleList() + self.act = torch.nn.functional.leaky_relu + if use_raw_node_label: + self.node_label_embedder = torch.nn.Linear(num_node_lab, gin_dimension) + + for n_1, n_2 in zip(dims[:-1], dims[1:]): + if conv_type == 'GIN': + l = gin_mlp_factory(gin_mlp_type, n_1, n_2) + self.convs.append(GINConv(l, train_eps=True)) + elif conv_type == 'GraphSAGE': + self.convs.append(SAGEConv(n_1, n_2)) + + self.bns.append(nn.BatchNorm1d(n_2)) + self.fc = None + self.classifier_extpers = ClassifierHead(dataset, gin_dimension, int(cls_hidden_dimension), + drop_out=drop_out) + + def forward2(self, h, batch): + h = global_add_pool(h, batch.batch) + out = self.classifier_extpers(h) + out = torch.nn.LogSoftmax(dim=1)(out) + return out + + def forward(self, batch, device): + + node_deg = batch.node_deg + if hasattr(batch, "node_lab"): + node_lab = batch.node_lab + else: + node_lab = None + edge_index = batch.edge_index + if self.use_raw_node_label: + if self.embed_deg is not None: + # tmp = [self.embed_deg(node_deg),self.node_label_embedder(node_lab.to(torch.float))] + tmp = [self.embed_deg(node_deg), batch.x] + else: + tmp = [] + raise ValueError + else: + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + if len(tmp) > 0: + tmp = torch.cat(tmp, dim=1) + else: + tmp = torch.tensor(tmp) + x = tmp + for conv, bn in zip(self.convs[:-1], self.bns[:-1]): + x = conv(x, edge_index) + x = bn(x) + x = self.act(x) + x = torch.nn.Dropout(p=0.5)(x) + x = self.convs[-1](x, edge_index) + + return x + + +def weight_reset(m): + if isinstance(m, nn.Linear): + m.reset_parameters() + + +class Filtration(torch.nn.Module): + def __init__(self, + dataset, + use_node_degree=None, + set_node_degree_uninformative=None, + use_node_label=None, + use_raw_node_label=None, + filt_conv_number=None, + filt_conv_dimension=None, + gin_mlp_type=None, + **kwargs + ): + super().__init__() + + dim = filt_conv_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if use_node_degree: + self.embed_deg = nn.Embedding(max_node_deg + 1, dim) + else: + self.embed_deg = None + + self.embed_lab = nn.Embedding(num_node_lab, dim) if (use_node_label and not use_raw_node_label and not (num_node_lab==0 or num_node_lab is None)) else None + self.use_raw_node_label = use_raw_node_label + + dim_input = dim*((self.embed_deg is not None) + (self.embed_lab is not None)) + + if (use_raw_node_label) and dataset[0].x is not None: + dim_input += dataset[0].x.size(1) + + dims = [dim_input] + (filt_conv_number) * [dim] + + self.convs = nn.ModuleList() + self.bns = nn.ModuleList() + self.act = torch.nn.functional.leaky_relu + if use_raw_node_label and dataset[0].x is not None: + self.node_label_embedder = torch.nn.Linear(dataset[0].x.size(1), filt_conv_dimension) + + for n_1, n_2 in zip(dims[:-1], dims[1:]): + l = gin_mlp_factory(gin_mlp_type, n_1, n_2) + self.convs.append(GINConv(l, train_eps=True)) + self.bns.append(nn.BatchNorm1d(n_2)) + + self.fc = nn.Sequential( + nn.Linear(sum(dims), dim), + # nn.Linear(dims[-1], dim), + nn.BatchNorm1d(dim), + nn.LeakyReLU(), + nn.Linear(dim, 1), + nn.Sigmoid() + ) + + if dim_input==0: + raise Exception("Cannot have neither degree nor node features") + elif 0 in dims: + raise Exception("Cannot have 0 dim hidden layers") + + def reset_parameters(self): + for conv in self.convs: + conv.reset_parameters() + self.fc.apply(weight_reset) + + def forward(self, batch): + node_deg = batch.node_deg + if hasattr(batch, "node_lab"): + node_lab = batch.node_lab + else: + node_lab = None + edge_index = batch.edge_index + if self.use_raw_node_label: + if self.embed_deg is not None and self.fc is not None: + # tmp = [self.embed_deg(node_deg),self.node_label_embedder(batch.x.to(torch.float))] + tmp = [self.embed_deg(node_deg), batch.x] # self.node_label_embedder(batch.x.to(torch.float))] + else: + tmp = [batch.x] + else: + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + tmp = torch.cat(tmp, dim=1) + + z = [tmp] + + for conv, bn in zip(self.convs, self.bns): + x = conv(z[-1], edge_index) + x = bn(x) + x = self.act(x) + z.append(x) + + x = torch.cat(z, dim=1) + ret = self.fc(x).squeeze() + return ret + + +class StandardPershomReadout(nn.Module): + def __init__(self, + dataset, + num_struct_elements=None, + cls_hidden_dimension=None, + drop_out=None, + ): + super().__init__() + assert isinstance(num_struct_elements, int) + # self.use_as_feature_extractor = True#False + + self.ldgm_0 = SLayerRationalHat(num_struct_elements, 2, radius_init=0.1) + self.ldgm_0_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + self.ldgm_1_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + fc_in_feat = num_struct_elements # 3*num_struct_elements + + self.cls_head = ClassifierHead( + dataset, + dim_in=fc_in_feat, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + def forward(self, h_0, h_0_ess, h_1_ess): + tmp = [] + + tmp.append(self.ldgm_0(h_0)) + tmp.append(self.ldgm_0_ess(h_0_ess)) + tmp.append(self.ldgm_1_ess(h_1_ess)) + x = torch.cat(tmp, dim=1) + return x + + +class PershomReadout(nn.Module): + def __init__(self, + dataset, + num_struct_elements=None, + cls_hidden_dimension=None, + drop_out=None, + ): + super().__init__() + assert isinstance(num_struct_elements, int) + # self.use_as_feature_extractor = True#False + + self.ldgm_0_up = SLayerRationalHat(num_struct_elements, 2, radius_init=0.1) + self.ldgm_0_down = SLayerRationalHat(num_struct_elements, 2, radius_init=0.1) + self.ldgm_cc = SLayerRationalHat(num_struct_elements, 2, + radius_init=0.1) # SLayerExponential(num_struct_elements, 2)#SLayerSquare(num_struct_elements, 2) + self.ldgm_h1 = SLayerRationalHat(num_struct_elements, 2, + radius_init=0.1) # SLayerExponential(num_struct_elements, 2)#SLayerSquare(num_struct_elements, 2) + + self.ldgm_0_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + self.ldgm_1_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + fc_in_feat = num_struct_elements # 3*num_struct_elements + + self.cls_head = ClassifierHead( + dataset, + dim_in=fc_in_feat, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + def forward(self, h_0_up, h_0_down, h_0_cc, h_1): + tmp = [] + + tmp.append(self.ldgm_0_up(h_0_up)) + tmp.append(self.ldgm_0_down(h_0_down)) + tmp.append(self.ldgm_cc(h_0_cc)) + tmp.append(self.ldgm_h1(h_1)) + + x = torch.cat(tmp, dim=1) + return x + + +class PershomClassifier(nn.Module): + def __init__(self, + dataset, + fc_in_feat=None, + cls_hidden_dimension=None, + drop_out=None, + ): + super().__init__() + # assert isinstance(num_struct_elements, int) + self.use_as_feature_extractor = False + self.cls_head = ClassifierHead( + dataset, + dim_in=fc_in_feat, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + def forward(self, x): + if not self.use_as_feature_extractor: + x = self.cls_head(x) + + return x + + +class PershomBase(nn.Module): + def __init__(self, aug): + super().__init__() + self.readout_type = "extph" + self.supervised = True + self.use_super_level_set_filtration = None + self.use_as_feature_extractor = False + self.cls = None + self.augmentor = aug + + self.convs = torch.nn.ModuleList() + self.mlp = None + self.classifier_gnn = None + self.classifier_extpers = None + self.classifier_standardpers = None + self.repr2structelems = None + self.structelems2repr = None + self.standard_ph_readout = None + self.epochs = 1 + self.p = 0.01 + self.gnn_node = None + + def compute_extended_ph_link_tree(self, node_filt, batch, device): + ph_input = [] + for idx, (i, j, e) in enumerate(zip(batch.sample_pos[:-1], batch.sample_pos[1:], batch.boundary_info)): + v = node_filt[i:j] # extract vertex values\ + v.to("cpu") + e.to("cpu") + # use this for visualization + if idx == 0 and len(batch.boundary_info) == 1: + # print("v: ",v) + # print("edge_index: ", batch.edge_index) + # print("x: ", batch.x) + pass + ph_input.append((v, [e])) + pers = ph_extended_link_tree(ph_input) # ph_input needs to be: (v,[e]) + + h_0_up = [torch.stack([x.to(device) for x in g[0]]) for g in pers] + h_0_down = [torch.stack([x.to(device) for x in g[1]]) for g in pers] + h_0_extplus = [torch.stack([x.to(device) for x in g[2]]) for g in pers] + h_1_extminus = [torch.stack([x.to(device) for x in g[3]]) for g in pers] + + return h_0_up, h_0_down, h_0_extplus, h_1_extminus + + def forward(self, batch, device): + assert self.use_super_level_set_filtration is not None + if batch.x is not None: + idx = torch.empty((batch.x.size(1),), dtype=torch.float32).uniform_(0, 1) < self.p + batch.x[:, idx] = 0 + node_filt0 = self.fil(batch) + ##h= self.gnn(batch, device) + if self.readout_type == "extph": + h_0_up_1, h_0_down_1, h_0_cc_1, h_1_1 = self.compute_extended_ph_link_tree(node_filt0, batch, device) + g1 = self.readout(h_0_up_1, h_0_down_1, h_0_cc_1, h_1_1) + elif self.readout_type == "sum": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_add_pool(node_filt0, batch.batch) + elif self.readout_type == "max": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_max_pool(node_filt0, batch.batch) + elif self.readout_type == "average": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_mean_pool(node_filt0, batch.batch) + elif self.readout_type == "sort": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_sort_pool(node_filt0, batch.batch, 100) + out = g1 + out = self.classifier_gnn(out) + out = torch.nn.LogSoftmax(dim=1)(out) + return out + + @property + def feature_dimension(self): + return self.cls.cls_head[0].in_features + + @property + def use_as_feature_extractor(self): + return self.use_as_feature_extractor + + @use_as_feature_extractor.setter + def use_as_feature_extractor(self, val): + if hasattr(self, 'cls'): + self.cls.use_as_feature_extractor = val + + def init_weights(self): + def init(m): + if isinstance(m, nn.Linear): + torch.nn.init.xavier_uniform_(m.weight) + # m.bias.data.fill_(0.01) + + self.apply(init) + + +class ClassicReadoutFilt(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool = None, + use_node_degree: bool = None, + set_node_degree_uninformative: bool = None, + use_node_label: bool = None, + use_raw_node_label: bool = None, + filt_conv_number: int = None, + filt_conv_dimension: int = None, + gin_mlp_type: str = None, + num_struct_elements: int = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + conv_number: int = None, + conv_dimension: int = None, + augmentor=(None, None), + readout="sum", + **kwargs, + ): + super().__init__(augmentor) + self.readout_type = readout + + self.use_super_level_set_filtration = use_super_level_set_filtration + + self.fil = Filtration( + dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + filt_conv_number=filt_conv_number, + filt_conv_dimension=filt_conv_dimension, + gin_mlp_type=gin_mlp_type, + ) + + self.standard_ph_readout = StandardPershomReadout( + dataset, + num_struct_elements=num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + + self.cls = PershomClassifier(dataset, + fc_in_feat=num_struct_elements, # 3*num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.gnn = ClassicGNN(dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + gin_number=conv_number, + conv_type='GIN', + gin_dimension=conv_dimension, + gin_mlp_type=gin_mlp_type, + ) + self.supervised = True + if self.readout_type == "sort": + self.classifier_gnn = ClassifierHead(dataset, 100, cls_hidden_dimension, drop_out=drop_out) + else: + self.classifier_gnn = ClassifierHead(dataset, 1, cls_hidden_dimension, drop_out=drop_out) + self.init_weights() + + def reset_parameters(self): + self.fil.reset_parameters() + self.classifier_gnn.apply(weight_reset) + + +class PershomLearnedFilt(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool = None, + use_node_degree: bool = None, + set_node_degree_uninformative: bool = None, + use_node_label: bool = None, + use_raw_node_label: bool = None, + gin_number: int = None, + gin_dimension: int = None, + gin_mlp_type: str = None, + num_struct_elements: int = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + augmentor=(None, None), + readout="extph", + **kwargs, + ): + super().__init__(augmentor) + + self.readout_type = "extph" + self.use_super_level_set_filtration = use_super_level_set_filtration + + self.fil = Filtration( + dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + gin_number=gin_number, + gin_dimension=gin_dimension, + gin_mlp_type=gin_mlp_type, + ) + + self.readout = PershomReadout( + dataset, + num_struct_elements=num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.readout.use_as_feature_extractor = True + self.cls = PershomClassifier(dataset, + fc_in_feat=num_struct_elements, # 3*num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.gnn = ClassicGNN(dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + gin_number=gin_number, + gin_dimension=gin_dimension, + gin_mlp_type=gin_mlp_type, + ) + + self.init_weights() + self.supervised = False + + +class PershomLearnedFiltSup(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool = None, + use_node_degree: bool = None, + set_node_degree_uninformative: bool = None, + use_node_label: bool = None, + use_raw_node_label: bool = None, + filt_conv_number: int = None, + filt_conv_dimension: int = None, + gin_mlp_type: str = None, + num_struct_elements: int = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + conv_number: int = None, + conv_dimension: int = None, + augmentor=(None, None), + readout="extph", + **kwargs, + ): + super().__init__(augmentor) + self.use_super_level_set_filtration = use_super_level_set_filtration + + self.fil = Filtration( + dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + filt_conv_number=filt_conv_number, + filt_conv_dimension=filt_conv_dimension, + gin_mlp_type=gin_mlp_type, + ) + + self.standard_ph_readout = StandardPershomReadout( + dataset, + num_struct_elements=num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + + self.readout = PershomReadout( + dataset, + num_struct_elements=num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.readout.use_as_feature_extractor = True + self.cls = PershomClassifier(dataset, + fc_in_feat=num_struct_elements, # 3*num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.gnn = ClassicGNN(dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + gin_number=conv_number, + conv_type='GIN', + gin_dimension=conv_dimension, + gin_mlp_type=gin_mlp_type, + ) + # self.mlp= nn.Linear(num_struct_elements*4,gin_dimension) + self.classifier_gnn = ClassifierHead(dataset, 4 * num_struct_elements, cls_hidden_dimension, drop_out=drop_out) + self.classifier_extpers = ClassifierHead(dataset, 4 * num_struct_elements, int(cls_hidden_dimension), + drop_out=drop_out) + self.classifier_standardpers = ClassifierHead(dataset, 3 * num_struct_elements, int(cls_hidden_dimension), + drop_out=drop_out) + + self.supervised = True + self.repr2structelems = nn.Linear(conv_dimension, 4 * num_struct_elements) + self.structelems2repr = nn.Linear(4 * num_struct_elements, conv_dimension) + self.init_weights() + + def reset_parameters(self): + self.fil.reset_parameters() + self.classifier_gnn.apply(weight_reset) + + +class PershomRigidDegreeFilt(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool = None, + num_struct_elements: int = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + **kwargs, + ): + super().__init__() + + self.use_super_level_set_filtration = use_super_level_set_filtration + + self.fil = DegreeOnlyFiltration() + + self.cls = PershomClassifier( + dataset, + drop_out=drop_out, + cls_hidden_dimension=cls_hidden_dimension + ) + + self.init_weights() + self.supervised = False + + +class OneHotEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + eye = torch.eye(dim, dtype=torch.float) + + self.register_buffer('eye', eye) + + def forward(self, batch): + assert batch.dtype == torch.long + + return self.eye.index_select(0, batch) + + @property + def dim(self): + return self.eye.size(1) + + +class UniformativeDummyEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + b = torch.ones(1, dim, dtype=torch.float) + self.register_buffer('ones', b) + + def forward(self, batch): + assert batch.dtype == torch.long + return self.ones.expand(batch.size(0), -1) + + @property + def dim(self): + return self.ones.size(1) + + +class GIN(nn.Module): + def __init__(self, + dataset, + use_node_degree: bool = None, + use_node_label: bool = None, + gin_number: int = None, + gin_dimension: int = None, + gin_mlp_type: str = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + set_node_degree_uninformative: bool = None, + pooling_strategy: str = None, + **kwargs, + ): + super().__init__() + self.use_as_feature_extractor = False + self.pooling_strategy = pooling_strategy + self.gin_dimension = gin_dimension + + dim = gin_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if set_node_degree_uninformative and use_node_degree: + self.embed_deg = UniformativeDummyEmbedding(gin_dimension) + elif use_node_degree: + self.embed_deg = OneHotEmbedding(max_node_deg + 1) + else: + self.embed_deg = None + + self.embed_lab = OneHotEmbedding(num_node_lab) if use_node_label else None + + dim_input = 0 + dim_input += self.embed_deg.dim if use_node_degree else 0 + dim_input += self.embed_lab.dim if use_node_label else 0 + assert dim_input > 0 + + dims = [dim_input] + (gin_number) * [dim] + self.convs = nn.ModuleList() + self.bns = nn.ModuleList() + self.act = torch.nn.functional.leaky_relu + + for n_1, n_2 in zip(dims[:-1], dims[1:]): + l = gin_mlp_factory(gin_mlp_type, n_1, n_2) + self.convs.append(GINConv(l, train_eps=True)) + self.bns.append(nn.BatchNorm1d(n_2)) + + if pooling_strategy == 'sum': + self.global_pool_fn = global_add_pool + elif pooling_strategy == 'sort': + self.k = int(np.percentile([d.num_nodes for d in dataset], 10)) + self.global_pool_fn = functools.partial(global_sort_pool, k=self.k) + self.sort_pool_nn = nn.Linear(self.k * gin_dimension, gin_dimension) + else: + raise ValueError + + self.cls = ClassifierHead( + dataset, + dim_in=gin_dimension, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + @property + def feature_dimension(self): + return self.cls.cls_head[0].in_features + + def forward(self, batch): + + node_deg = batch.node_deg + node_lab = batch.node_lab + + edge_index = batch.edge_index + + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + tmp = torch.cat(tmp, dim=1) + + z = [tmp] + + for conv, bn in zip(self.convs, self.bns): + x = conv(z[-1], edge_index) + x = bn(x) + x = self.act(x) + z.append(x) + + x = z[-1] + x = self.global_pool_fn(x, batch.batch) + + if self.pooling_strategy == 'sort': + x = self.sort_pool_nn(x) + x = x.squeeze() + + if not self.use_as_feature_extractor: + x = self.cls(x) + + return x + + +class SimpleNNBaseline(nn.Module): + def __init__(self, + dataset, + use_node_degree: bool = None, + use_node_label: bool = None, + set_node_degree_uninformative: bool = None, + gin_dimension: int = None, + gin_mlp_type: str = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + pooling_strategy: str = None, + **kwargs, + ): + super().__init__() + self.use_as_feature_extractor = False + self.pooling_strategy = pooling_strategy + self.gin_dimension = gin_dimension + + dim = gin_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if set_node_degree_uninformative and use_node_degree: + self.embed_deg = UniformativeDummyEmbedding(gin_dimension) + elif use_node_degree: + self.embed_deg = OneHotEmbedding(max_node_deg + 1) + else: + self.embed_deg = None + + self.embed_lab = OneHotEmbedding(num_node_lab) if use_node_label else None + + dim_input = 0 + dim_input += self.embed_deg.dim if use_node_degree else 0 + dim_input += self.embed_lab.dim if use_node_label else 0 + assert dim_input > 0 + + self.mlp = gin_mlp_factory(gin_mlp_type, dim_input, dim) + + if pooling_strategy == 'sum': + self.global_pool_fn = global_add_pool + elif pooling_strategy == 'sort': + self.k = int(np.percentile([d.num_nodes for d in dataset], 10)) + self.global_pool_fn = functools.partial(global_sort_pool, k=self.k) + self.sort_pool_nn = nn.Linear(self.k * gin_dimension, gin_dimension) + else: + raise ValueError + + self.cls = ClassifierHead( + dataset, + dim_in=gin_dimension, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + @property + def feature_dimension(self): + return self.cls.cls_head[0].in_features + + def forward(self, batch): + + node_deg = batch.node_deg + node_lab = batch.node_lab + + edge_index = batch.edge_index + + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + x = torch.cat(tmp, dim=1) + + x = self.mlp(x) + x = self.global_pool_fn(x, batch.batch) + + if self.pooling_strategy == 'sort': + x = self.sort_pool_nn(x) + x = x.squeeze() + + if not self.use_as_feature_extractor: + x = self.cls(x) + + return x + diff --git a/gnn/imdbmulti_model.py b/gnn/imdbmulti_model.py new file mode 100644 index 0000000..7639d47 --- /dev/null +++ b/gnn/imdbmulti_model.py @@ -0,0 +1,898 @@ +import torch +import numpy as np +import torch.nn as nn + +import functools + +from torchPHext.torchex_PHext.nn import SLayerRationalHat, SLayerSquare, SLayerExponential +from torch_geometric.nn import SAGEConv, LEConv, GINConv, global_add_pool, global_sort_pool, global_mean_pool, \ + global_max_pool +from torchPHext.torchex_PHext import pershom as pershom_ext +from torch_geometric.nn import GINConv, global_add_pool +import sys + +sys.path.append(".") + +ph_extended_link_tree = pershom_ext.pershom_backend.__C.VertExtendedFiltCompCuda_link_cut_tree__extended_persistence_batch + + +def gin_mlp_factory(gin_mlp_type: str, dim_in: int, dim_out: int): + if gin_mlp_type == 'lin': + return nn.Linear(dim_in, dim_out) + + elif gin_mlp_type == 'lin_lrelu_lin': + return nn.Sequential( + nn.Linear(dim_in, dim_in), + nn.LeakyReLU(), + nn.Linear(dim_in, dim_out) + ) + + elif gin_mlp_type == 'lin_bn_lrelu_lin': + return nn.Sequential( + nn.Linear(dim_in, dim_in), + nn.BatchNorm1d(dim_in), + nn.LeakyReLU(), + nn.Linear(dim_in, dim_out) + ) + else: + raise ValueError("Unknown gin_mlp_type!") + + +def ClassifierHead( + dataset, + dim_in: int = None, + hidden_dim: int = None, + drop_out: float = None): + assert (0.0 <= drop_out) and (drop_out < 1.0) + assert dim_in is not None + assert drop_out is not None + assert hidden_dim is not None + + tmp = [ + nn.Linear(dim_in, hidden_dim), + nn.LeakyReLU(), + ] + + if drop_out > 0: + tmp += [nn.Dropout(p=drop_out)] + + tmp += [nn.Linear(hidden_dim, dataset.num_classes)] + + return nn.Sequential(*tmp) + + +class DegreeOnlyFiltration(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, batch): + tmp = [] + for i, j in zip(batch.sample_pos[:-1], batch.sample_pos[1:]): + max_deg = batch.node_deg[i:j].max() + + t = torch.ones(j - i, dtype=torch.float, device=batch.node_deg.device) + t = t * max_deg + tmp.append(t) + + max_deg = torch.cat(tmp, dim=0) + + normalized_node_deg = batch.node_deg.float() / max_deg + + return normalized_node_deg + + +class ClassicGNN(torch.nn.Module): + def __init__(self, + dataset, + use_node_degree=None, + set_node_degree_uninformative=None, + use_node_label=None, + use_raw_node_label=None, + gin_number=None, + gin_dimension=None, + conv_type='GIN', + gin_mlp_type=None, + cls_hidden_dimension=512, + drop_out=0.5, + **kwargs + ): + super().__init__() + + dim = gin_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if set_node_degree_uninformative and use_node_degree: + self.embed_deg = UniformativeDummyEmbedding(gin_dimension) + elif use_node_degree: + self.embed_deg = nn.Embedding(max_node_deg + 1, dim) + else: + self.embed_deg = None + + self.embed_lab = nn.Embedding(num_node_lab, dim) if use_node_label else None + self.use_raw_node_label = use_raw_node_label + + dim_input = dim*((self.embed_deg is not None) + (self.embed_lab is not None)) + + if (use_raw_node_label) and dataset[0].x is not None: + dim_input += dataset[0].x.size(1) + + dims = [dim_input] + (gin_number) * [dim] + + self.convs = nn.ModuleList() + self.bns = nn.ModuleList() + self.act = torch.nn.functional.leaky_relu + if use_raw_node_label and not num_node_lab==0: + self.node_label_embedder = torch.nn.Linear(num_node_lab, gin_dimension) + + for n_1, n_2 in zip(dims[:-1], dims[1:]): + if conv_type == 'GIN': + l = gin_mlp_factory(gin_mlp_type, n_1, n_2) + self.convs.append(GINConv(l, train_eps=True)) + elif conv_type == 'GraphSAGE': + self.convs.append(SAGEConv(n_1, n_2)) + + self.bns.append(nn.BatchNorm1d(n_2)) + self.fc = None + self.classifier_extpers = ClassifierHead(dataset, gin_dimension, int(cls_hidden_dimension), + drop_out=drop_out) + + def forward2(self, h, batch): + h = global_add_pool(h, batch.batch) + out = self.classifier_extpers(h) + out = torch.nn.LogSoftmax(dim=1)(out) + return out + + def forward(self, batch, device): + + node_deg = batch.node_deg + if hasattr(batch, "node_lab"): + node_lab = batch.node_lab + else: + node_lab = None + edge_index = batch.edge_index + if self.use_raw_node_label: + if self.embed_deg is not None: + # tmp = [self.embed_deg(node_deg),self.node_label_embedder(node_lab.to(torch.float))] + tmp = [self.embed_deg(node_deg), batch.x] + else: + tmp = [] + raise ValueError + else: + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + if len(tmp) > 0: + tmp = torch.cat(tmp, dim=1) + else: + tmp = torch.tensor(tmp) + x = tmp + for conv, bn in zip(self.convs[:-1], self.bns[:-1]): + x = conv(x, edge_index) + x = bn(x) + x = self.act(x) + x = torch.nn.Dropout(p=0.5)(x) + x = self.convs[-1](x, edge_index) + + return x + + +def weight_reset(m): + if isinstance(m, nn.Linear): + m.reset_parameters() + + +class Filtration(torch.nn.Module): + def __init__(self, + dataset, + use_node_degree=None, + set_node_degree_uninformative=None, + use_node_label=None, + use_raw_node_label=None, + filt_conv_number=None, + filt_conv_dimension=None, + gin_mlp_type=None, + **kwargs + ): + super().__init__() + + dim = filt_conv_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if use_node_degree: + self.embed_deg = nn.Embedding(max_node_deg + 1, dim) + else: + self.embed_deg = None + + self.embed_lab = nn.Embedding(num_node_lab, dim) if (use_node_label and not use_raw_node_label and not ( + num_node_lab == 0 or num_node_lab is None)) else None + self.use_raw_node_label = use_raw_node_label + + dim_input = dim*((self.embed_deg is not None) + (self.embed_lab is not None)) + + if (use_raw_node_label) and dataset[0].x is not None: + dim_input += dataset[0].x.size(1) + + dims = [dim_input] + (filt_conv_number) * [dim] + + self.convs = nn.ModuleList() + self.bns = nn.ModuleList() + self.act = torch.nn.functional.leaky_relu + if use_raw_node_label and dataset[0].x is not None: + self.node_label_embedder = torch.nn.Linear(dataset[0].x.size(1), filt_conv_dimension) + + for n_1, n_2 in zip(dims[:-1], dims[1:]): + l = gin_mlp_factory(gin_mlp_type, n_1, n_2) + self.convs.append(GINConv(l, train_eps=True)) + self.bns.append(nn.BatchNorm1d(n_2)) + + self.fc = nn.Sequential( + nn.Linear(sum(dims), dim), + # nn.Linear(dims[-1], dim), + nn.BatchNorm1d(dim), + nn.LeakyReLU(), + nn.Linear(dim, 1), + nn.Sigmoid() + ) + + if dim_input==0: + raise Exception("Cannot have neither degree nor node features") + elif 0 in dims: + raise Exception("Cannot have 0 dim hidden layers") + + def reset_parameters(self): + for conv in self.convs: + conv.reset_parameters() + self.fc.apply(weight_reset) + + def forward(self, batch): + node_deg = batch.node_deg + if hasattr(batch, "node_lab"): + node_lab = batch.node_lab + else: + node_lab = None + edge_index = batch.edge_index + if self.use_raw_node_label and batch.x is not None: + if self.embed_deg is not None and self.fc is not None: + tmp = [self.embed_deg(node_deg), batch.x] # self.node_label_embedder(batch.x.to(torch.float))] + else: + tmp = [batch.x] + else: + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + tmp = torch.cat(tmp, dim=1) + + z = [tmp] + + for conv, bn in zip(self.convs, self.bns): + x = conv(z[-1], edge_index) + x = bn(x) + x = self.act(x) + z.append(x) + + x = torch.cat(z, dim=1) + ret = self.fc(x).squeeze() + return ret + + +class StandardPershomReadout(nn.Module): + def __init__(self, + dataset, + num_struct_elements=None, + cls_hidden_dimension=None, + drop_out=None, + ): + super().__init__() + assert isinstance(num_struct_elements, int) + # self.use_as_feature_extractor = True#False + + self.ldgm_0 = SLayerRationalHat(num_struct_elements, 2, radius_init=0.1) + self.ldgm_0_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + self.ldgm_1_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + fc_in_feat = num_struct_elements # 3*num_struct_elements + + self.cls_head = ClassifierHead( + dataset, + dim_in=fc_in_feat, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + def forward(self, h_0, h_0_ess, h_1_ess): + tmp = [] + + tmp.append(self.ldgm_0(h_0)) + tmp.append(self.ldgm_0_ess(h_0_ess)) + tmp.append(self.ldgm_1_ess(h_1_ess)) + x = torch.cat(tmp, dim=1) + return x + + +class PershomReadout(nn.Module): + def __init__(self, + dataset, + num_struct_elements=None, + cls_hidden_dimension=None, + drop_out=None, + ): + super().__init__() + assert isinstance(num_struct_elements, int) + # self.use_as_feature_extractor = True#False + + self.ldgm_0_up = SLayerRationalHat(num_struct_elements, 2, radius_init=0.1) + self.ldgm_0_down = SLayerRationalHat(num_struct_elements, 2, radius_init=0.1) + self.ldgm_cc = SLayerRationalHat(num_struct_elements, 2, + radius_init=0.1) # SLayerExponential(num_struct_elements, 2)#SLayerSquare(num_struct_elements, 2) + self.ldgm_h1 = SLayerRationalHat(num_struct_elements, 2, + radius_init=0.1) # SLayerExponential(num_struct_elements, 2)#SLayerSquare(num_struct_elements, 2) + + self.ldgm_0_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + self.ldgm_1_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + fc_in_feat = num_struct_elements # 3*num_struct_elements + + self.cls_head = ClassifierHead( + dataset, + dim_in=fc_in_feat, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + def forward(self, h_0_up, h_0_down, h_0_cc, h_1): + tmp = [] + + tmp.append(self.ldgm_0_up(h_0_up)) + tmp.append(self.ldgm_0_down(h_0_down)) + tmp.append(self.ldgm_cc(h_0_cc)) + tmp.append(self.ldgm_h1(h_1)) + + x = torch.cat(tmp, dim=1) + return x + + +class PershomClassifier(nn.Module): + def __init__(self, + dataset, + fc_in_feat=None, + cls_hidden_dimension=None, + drop_out=None, + ): + super().__init__() + # assert isinstance(num_struct_elements, int) + self.use_as_feature_extractor = False + self.cls_head = ClassifierHead( + dataset, + dim_in=fc_in_feat, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + def forward(self, x): + if not self.use_as_feature_extractor: + x = self.cls_head(x) + + return x + + +class PershomBase(nn.Module): + def __init__(self, aug): + super().__init__() + self.readout_type = "extph" + self.supervised = True + self.use_super_level_set_filtration = None + self.use_as_feature_extractor = False + self.cls = None + self.augmentor = aug + + self.convs = torch.nn.ModuleList() + self.mlp = None + self.classifier_gnn = None + self.classifier_extpers = None + self.classifier_standardpers = None + self.repr2structelems = None + self.structelems2repr = None + self.standard_ph_readout = None + self.epochs = 1 + self.p = 0.01 + self.gnn_node = None + + def compute_extended_ph_link_tree(self, node_filt, batch, device): + ph_input = [] + for idx, (i, j, e) in enumerate(zip(batch.sample_pos[:-1], batch.sample_pos[1:], batch.boundary_info)): + v = node_filt[i:j] # extract vertex values\ + v.to("cpu") + e.to("cpu") + # use this for visualization + if idx == 0 and len(batch.boundary_info) == 1: + # print("v: ",v) + # print("edge_index: ", batch.edge_index) + # print("x: ", batch.x) + pass + ph_input.append((v, [e])) + pers = ph_extended_link_tree(ph_input) # ph_input needs to be: (v,[e]) + + h_0_up = [torch.stack([x.to(device) for x in g[0]]) for g in pers] + h_0_down = [torch.stack([x.to(device) for x in g[1]]) for g in pers] + h_0_extplus = [torch.stack([x.to(device) for x in g[2]]) for g in pers] + h_1_extminus = [torch.stack([x.to(device) for x in g[3]]) for g in pers] + + return h_0_up, h_0_down, h_0_extplus, h_1_extminus + + def forward(self, batch, device): + if batch.x is not None: + idx = torch.empty((batch.x.size(1),), dtype=torch.float32).uniform_(0, 1) < self.p + batch.x[:, idx] = 0 + node_filt0 = self.fil(batch) + ##h= self.gnn(batch, device) + if self.readout_type == "extph": + h_0_up_1, h_0_down_1, h_0_cc_1, h_1_1 = self.compute_extended_ph_link_tree(node_filt0, batch, device) + g1 = self.readout(h_0_up_1, h_0_down_1, h_0_cc_1, h_1_1) + elif self.readout_type == "sum": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_add_pool(node_filt0, batch.batch) + elif self.readout_type == "max": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_max_pool(node_filt0, batch.batch) + elif self.readout_type == "average": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_mean_pool(node_filt0, batch.batch) + elif self.readout_type == "sort": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_sort_pool(node_filt0, batch.batch, 400) + out = g1 + out = self.classifier_gnn(out) + out = torch.nn.LogSoftmax(dim=1)(out) + return out + + @property + def feature_dimension(self): + return self.cls.cls_head[0].in_features + + @property + def use_as_feature_extractor(self): + return self.use_as_feature_extractor + + @use_as_feature_extractor.setter + def use_as_feature_extractor(self, val): + if hasattr(self, 'cls'): + self.cls.use_as_feature_extractor = val + + def init_weights(self): + def init(m): + if isinstance(m, nn.Linear): + torch.nn.init.xavier_uniform_(m.weight) + # m.bias.data.fill_(0.01) + + self.apply(init) + + +class ClassicReadoutFilt(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool = None, + use_node_degree: bool = None, + set_node_degree_uninformative: bool = None, + use_node_label: bool = None, + use_raw_node_label: bool = None, + filt_conv_number: int = None, + filt_conv_dimension: int = None, + gin_mlp_type: str = None, + num_struct_elements: int = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + conv_number: int = None, + conv_dimension: int = None, + augmentor=(None, None), + readout="sum", + **kwargs, + ): + super().__init__(augmentor) + self.readout_type = readout + + self.use_super_level_set_filtration = use_super_level_set_filtration + + self.fil = Filtration( + dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + filt_conv_number=filt_conv_number, + filt_conv_dimension=filt_conv_dimension, + gin_mlp_type=gin_mlp_type, + ) + + self.standard_ph_readout = StandardPershomReadout( + dataset, + num_struct_elements=num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + + self.cls = PershomClassifier(dataset, + fc_in_feat=num_struct_elements, # 3*num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.gnn = ClassicGNN(dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + gin_number=conv_number, + conv_type='GIN', + gin_dimension=conv_dimension, + gin_mlp_type=gin_mlp_type, + ) + self.supervised = True + if self.readout_type == "sort": + self.classifier_gnn = ClassifierHead(dataset, 400, cls_hidden_dimension, drop_out=drop_out) + else: + self.classifier_gnn = ClassifierHead(dataset, 1, cls_hidden_dimension, drop_out=drop_out) + self.init_weights() + + def reset_parameters(self): + self.fil.reset_parameters() + self.classifier_gnn.apply(weight_reset) + + +class PershomLearnedFilt(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool = None, + use_node_degree: bool = None, + set_node_degree_uninformative: bool = None, + use_node_label: bool = None, + use_raw_node_label: bool = None, + gin_number: int = None, + gin_dimension: int = None, + gin_mlp_type: str = None, + num_struct_elements: int = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + augmentor=(None, None), + readout="extph", + **kwargs, + ): + super().__init__(augmentor) + + self.readout_type = "extph" + self.use_super_level_set_filtration = use_super_level_set_filtration + + self.fil = Filtration( + dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + gin_number=gin_number, + gin_dimension=gin_dimension, + gin_mlp_type=gin_mlp_type, + ) + + self.readout = PershomReadout( + dataset, + num_struct_elements=num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.readout.use_as_feature_extractor = True + self.cls = PershomClassifier(dataset, + fc_in_feat=num_struct_elements, # 3*num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.gnn = ClassicGNN(dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + gin_number=gin_number, + gin_dimension=gin_dimension, + gin_mlp_type=gin_mlp_type, + ) + + self.init_weights() + self.supervised = False + + +class PershomLearnedFiltSup(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool = None, + use_node_degree: bool = None, + set_node_degree_uninformative: bool = None, + use_node_label: bool = None, + use_raw_node_label: bool = None, + filt_conv_number: int = None, + filt_conv_dimension: int = None, + gin_mlp_type: str = None, + num_struct_elements: int = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + conv_number: int = None, + conv_dimension: int = None, + augmentor=(None, None), + readout="extph", + **kwargs, + ): + super().__init__(augmentor) + + self.use_super_level_set_filtration = use_super_level_set_filtration + + self.fil = Filtration( + dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + filt_conv_number=filt_conv_number, + filt_conv_dimension=filt_conv_dimension, + gin_mlp_type=gin_mlp_type, + ) + + self.readout = PershomReadout( + dataset, + num_struct_elements=num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.readout.use_as_feature_extractor = True + self.classifier_gnn = ClassifierHead(dataset, 4 * num_struct_elements, cls_hidden_dimension, drop_out=drop_out) + + self.supervised = True + self.init_weights() + + def reset_parameters(self): + self.fil.reset_parameters() + self.classifier_gnn.apply(weight_reset) + + +class PershomRigidDegreeFilt(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool = None, + num_struct_elements: int = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + **kwargs, + ): + super().__init__() + + self.use_super_level_set_filtration = use_super_level_set_filtration + + self.fil = DegreeOnlyFiltration() + + self.cls = PershomClassifier( + dataset, + drop_out=drop_out, + cls_hidden_dimension=cls_hidden_dimension + ) + + self.init_weights() + self.supervised = False + + +class OneHotEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + eye = torch.eye(dim, dtype=torch.float) + + self.register_buffer('eye', eye) + + def forward(self, batch): + assert batch.dtype == torch.long + + return self.eye.index_select(0, batch) + + @property + def dim(self): + return self.eye.size(1) + + +class UniformativeDummyEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + b = torch.ones(1, dim, dtype=torch.float) + self.register_buffer('ones', b) + + def forward(self, batch): + assert batch.dtype == torch.long + return self.ones.expand(batch.size(0), -1) + + @property + def dim(self): + return self.ones.size(1) + + +class GIN(nn.Module): + def __init__(self, + dataset, + use_node_degree: bool = None, + use_node_label: bool = None, + gin_number: int = None, + gin_dimension: int = None, + gin_mlp_type: str = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + set_node_degree_uninformative: bool = None, + pooling_strategy: str = None, + **kwargs, + ): + super().__init__() + self.use_as_feature_extractor = False + self.pooling_strategy = pooling_strategy + self.gin_dimension = gin_dimension + + dim = gin_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if set_node_degree_uninformative and use_node_degree: + self.embed_deg = UniformativeDummyEmbedding(gin_dimension) + elif use_node_degree: + self.embed_deg = OneHotEmbedding(max_node_deg + 1) + else: + self.embed_deg = None + + self.embed_lab = OneHotEmbedding(num_node_lab) if use_node_label else None + + dim_input = 0 + dim_input += self.embed_deg.dim if use_node_degree else 0 + dim_input += self.embed_lab.dim if use_node_label else 0 + assert dim_input > 0 + + dims = [dim_input] + (gin_number) * [dim] + self.convs = nn.ModuleList() + self.bns = nn.ModuleList() + self.act = torch.nn.functional.leaky_relu + + for n_1, n_2 in zip(dims[:-1], dims[1:]): + l = gin_mlp_factory(gin_mlp_type, n_1, n_2) + self.convs.append(GINConv(l, train_eps=True)) + self.bns.append(nn.BatchNorm1d(n_2)) + + if pooling_strategy == 'sum': + self.global_pool_fn = global_add_pool + elif pooling_strategy == 'sort': + self.k = int(np.percentile([d.num_nodes for d in dataset], 10)) + self.global_pool_fn = functools.partial(global_sort_pool, k=self.k) + self.sort_pool_nn = nn.Linear(self.k * gin_dimension, gin_dimension) + else: + raise ValueError + + self.cls = ClassifierHead( + dataset, + dim_in=gin_dimension, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + @property + def feature_dimension(self): + return self.cls.cls_head[0].in_features + + def forward(self, batch): + + node_deg = batch.node_deg + node_lab = batch.node_lab + + edge_index = batch.edge_index + + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + tmp = torch.cat(tmp, dim=1) + + z = [tmp] + + for conv, bn in zip(self.convs, self.bns): + x = conv(z[-1], edge_index) + x = bn(x) + x = self.act(x) + z.append(x) + + x = z[-1] + x = self.global_pool_fn(x, batch.batch) + + if self.pooling_strategy == 'sort': + x = self.sort_pool_nn(x) + x = x.squeeze() + + if not self.use_as_feature_extractor: + x = self.cls(x) + + return x + + +class SimpleNNBaseline(nn.Module): + def __init__(self, + dataset, + use_node_degree: bool = None, + use_node_label: bool = None, + set_node_degree_uninformative: bool = None, + gin_dimension: int = None, + gin_mlp_type: str = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + pooling_strategy: str = None, + **kwargs, + ): + super().__init__() + self.use_as_feature_extractor = False + self.pooling_strategy = pooling_strategy + self.gin_dimension = gin_dimension + + dim = gin_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if set_node_degree_uninformative and use_node_degree: + self.embed_deg = UniformativeDummyEmbedding(gin_dimension) + elif use_node_degree: + self.embed_deg = OneHotEmbedding(max_node_deg + 1) + else: + self.embed_deg = None + + self.embed_lab = OneHotEmbedding(num_node_lab) if use_node_label else None + + dim_input = 0 + dim_input += self.embed_deg.dim if use_node_degree else 0 + dim_input += self.embed_lab.dim if use_node_label else 0 + assert dim_input > 0 + + self.mlp = gin_mlp_factory(gin_mlp_type, dim_input, dim) + + if pooling_strategy == 'sum': + self.global_pool_fn = global_add_pool + elif pooling_strategy == 'sort': + self.k = int(np.percentile([d.num_nodes for d in dataset], 10)) + self.global_pool_fn = functools.partial(global_sort_pool, k=self.k) + self.sort_pool_nn = nn.Linear(self.k * gin_dimension, gin_dimension) + else: + raise ValueError + + self.cls = ClassifierHead( + dataset, + dim_in=gin_dimension, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + @property + def feature_dimension(self): + return self.cls.cls_head[0].in_features + + def forward(self, batch): + + node_deg = batch.node_deg + node_lab = batch.node_lab + + edge_index = batch.edge_index + + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + x = torch.cat(tmp, dim=1) + + x = self.mlp(x) + x = self.global_pool_fn(x, batch.batch) + + if self.pooling_strategy == 'sort': + x = self.sort_pool_nn(x) + x = x.squeeze() + + if not self.use_as_feature_extractor: + x = self.cls(x) + + return x + diff --git a/gnn/mol_model.py b/gnn/mol_model.py new file mode 100644 index 0000000..4e251e4 --- /dev/null +++ b/gnn/mol_model.py @@ -0,0 +1,1013 @@ +import torch +import numpy as np +import torch.nn as nn + +import functools + +from torchPHext.torchex_PHext.nn import SLayerRationalHat, SLayerSquare, SLayerExponential +from torch_geometric.nn import SAGEConv, LEConv, GINConv, global_add_pool, global_sort_pool, global_mean_pool, \ + global_max_pool + +from torchPHext.torchex_PHext import pershom as pershom_ext +from torch_geometric.nn import GINConv, global_add_pool +import sys + +sys.path.append(".") + +from .conv import GNN_node_Virtualnode +ph_extended_link_tree = pershom_ext.pershom_backend.__C.VertExtendedFiltCompCuda_link_cut_tree__extended_persistence_batch + +def gin_mlp_factory(gin_mlp_type: str, dim_in: int, dim_out: int): + if gin_mlp_type == 'lin': + return nn.Linear(dim_in, dim_out) + + elif gin_mlp_type == 'lin_lrelu_lin': + return nn.Sequential( + nn.Linear(dim_in, dim_in), + nn.LeakyReLU(), + nn.Linear(dim_in, dim_out) + ) + + elif gin_mlp_type == 'lin_bn_lrelu_lin': + return nn.Sequential( + nn.Linear(dim_in, dim_in), + nn.BatchNorm1d(dim_in), + nn.LeakyReLU(), + nn.Linear(dim_in, dim_out) + ) + else: + raise ValueError("Unknown gin_mlp_type!") + + +def ClassifierHead( + dataset, + dim_in: int = None, + hidden_dim: int = None, + drop_out: float = None): + assert (0.0 <= drop_out) and (drop_out < 1.0) + assert dim_in is not None + assert drop_out is not None + assert hidden_dim is not None + + tmp = [ + nn.Linear(dim_in, hidden_dim), + nn.LeakyReLU(), + ] + + if drop_out > 0: + tmp += [nn.Dropout(p=drop_out)] + + tmp += [nn.Linear(hidden_dim, dataset.num_classes)] + + return nn.Sequential(*tmp) + +class DegreeOnlyFiltration(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, batch): + tmp = [] + for i, j in zip(batch.sample_pos[:-1], batch.sample_pos[1:]): + max_deg = batch.node_deg[i:j].max() + + t = torch.ones(j - i, dtype=torch.float, device=batch.node_deg.device) + t = t * max_deg + tmp.append(t) + + max_deg = torch.cat(tmp, dim=0) + + normalized_node_deg = batch.node_deg.float() / max_deg + + return normalized_node_deg + +class ClassicGNN(torch.nn.Module): + def __init__(self, + dataset, + use_node_degree=None, + set_node_degree_uninformative=None, + use_node_label=None, + use_raw_node_label=None, + gin_number=None, + gin_dimension=None, + conv_type='GIN', + gin_mlp_type=None, + cls_hidden_dimension=512, + drop_out=0.5, + **kwargs + ): + super().__init__() + + dim = gin_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if set_node_degree_uninformative and use_node_degree: + self.embed_deg = UniformativeDummyEmbedding(gin_dimension) + elif use_node_degree: + self.embed_deg = nn.Embedding(max_node_deg + 1, dim) + else: + self.embed_deg = None + + self.embed_lab = nn.Embedding(num_node_lab, dim) if use_node_label else None + self.use_raw_node_label = use_raw_node_label + + dim_input = dim*((self.embed_deg is not None) + (self.embed_lab is not None)) + #dim_input = dim * ((self.embed_deg is not None)) + if (use_node_label and use_raw_node_label): + dim_input += dataset[0].x.size(1) + + dims = [dim_input] + (gin_number) * [dim] + + self.convs = nn.ModuleList() + self.bns = nn.ModuleList() + self.act = torch.nn.functional.leaky_relu + if use_raw_node_label: + self.node_label_embedder = torch.nn.Linear(num_node_lab, gin_dimension) + + for n_1, n_2 in zip(dims[:-1], dims[1:]): + if conv_type == 'GIN': + l = gin_mlp_factory(gin_mlp_type, n_1, n_2) + self.convs.append(GINConv(l, train_eps=True)) + elif conv_type == 'GraphSAGE': + self.convs.append(SAGEConv(n_1, n_2)) + + self.bns.append(nn.BatchNorm1d(n_2)) + self.fc = None + self.classifier_extpers = ClassifierHead(dataset, gin_dimension, int(cls_hidden_dimension), + drop_out=drop_out) + + def forward2(self, h, batch): + h = global_add_pool(h, batch.batch) + out = self.classifier_extpers(h) + out = torch.nn.LogSoftmax(dim=1)(out) + return out + + def forward(self, batch, device): + + node_deg = batch.node_deg + if hasattr(batch, "node_lab"): + node_lab = batch.node_lab + else: + node_lab = None + edge_index = batch.edge_index + if self.use_raw_node_label: + if self.embed_deg is not None: + # tmp = [self.embed_deg(node_deg),self.node_label_embedder(node_lab.to(torch.float))] + tmp = [self.embed_deg(node_deg), batch.x] + else: + tmp = [] + raise ValueError + else: + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + if len(tmp) > 0: + tmp = torch.cat(tmp, dim=1) + else: + tmp = torch.tensor(tmp) + x = tmp + for conv, bn in zip(self.convs[:-1], self.bns[:-1]): + x = conv(x, edge_index) + x = bn(x) + x = self.act(x) + x = torch.nn.Dropout(p=0.5)(x) + x = self.convs[-1](x, edge_index) + + return x + + +def weight_reset(m): + if isinstance(m, nn.Linear): + m.reset_parameters() + +class MolFiltration(torch.nn.Module): + def __init__(self, + dataset, + use_node_degree=None, + set_node_degree_uninformative=None, + use_node_label=None, + use_raw_node_label=None, + filt_conv_number=None, + filt_conv_dimension=None, + gin_mlp_type=None, + **kwargs + ): + super().__init__() + + dim = filt_conv_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if use_node_degree: + self.embed_deg = nn.Embedding(max_node_deg + 1, dim) + else: + self.embed_deg = None + + self.embed_lab = nn.Embedding(num_node_lab, dim) if (use_node_label and not use_raw_node_label and not (num_node_lab==0 or num_node_lab is None)) else None + self.use_raw_node_label = use_raw_node_label + + dim_input = dim + if use_node_degree: + dim_input+= dim + dims = [dim_input] + (filt_conv_number) * [dim] + + self.fc = nn.Sequential( + nn.Linear(sum(dims), dim), + # nn.Linear(dims[-1], dim), + nn.BatchNorm1d(dim), + nn.LeakyReLU(), + nn.Linear(dim, 1), + nn.Sigmoid() + ) + + if dim_input==0: + raise Exception("Cannot have neither degree nor node features") + elif 0 in dims: + raise Exception("Cannot have 0 dim hidden layers") + self.gnn_node = GNN_node_Virtualnode(filt_conv_number, filt_conv_dimension) + + def reset_parameters(self): + # for conv in self.gnn_node.convs: + # conv.reset_parameters() + for mlp in self.gnn_node.mlp_virtualnode_list: + mlp.reset_parameters() + + def forward2(self, h, batch): + h = global_add_pool(h, batch.batch) + out = self.classifier_extpers(h) + out = torch.nn.LogSoftmax(dim=1)(out) + return out + + def forward(self, batch): + assert(hasattr(batch, "x") and batch.x is not None) + #if hasattr(batch, "x") and batch.x is not None: + h = self.gnn_node((batch.x, batch.edge_index, batch.edge_attr, batch.batch, self.embed_deg(batch.node_deg))) + # else: + # h = self.gnn_node((self.embed_deg, batch.edge_index, batch.edge_attr, batch.batch)) + return h + +class Filtration(torch.nn.Module): + def __init__(self, + dataset, + use_node_degree=None, + set_node_degree_uninformative=None, + use_node_label=None, + use_raw_node_label=None, + filt_conv_number=None, + filt_conv_dimension=None, + gin_mlp_type=None, + **kwargs + ): + super().__init__() + + dim = filt_conv_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if use_node_degree: + self.embed_deg = nn.Embedding(max_node_deg + 1, dim) + else: + self.embed_deg = None + + self.embed_lab = nn.Embedding(num_node_lab, dim) if (use_node_label and not use_raw_node_label and not (num_node_lab==0 or num_node_lab is None)) else None + self.use_raw_node_label = use_raw_node_label + + dim_input = dim*((self.embed_deg is not None) + (self.embed_lab is not None)) + + if (use_raw_node_label) and dataset[0].x is not None: + dim_input += dataset[0].x.size(1) + + dims = [dim_input] + (filt_conv_number) * [dim] + + self.convs = nn.ModuleList() + self.bns = nn.ModuleList() + self.act = torch.nn.functional.leaky_relu + if use_raw_node_label and dataset[0].x is not None: + self.node_label_embedder = torch.nn.Linear(dataset[0].x.size(1), filt_conv_dimension) + + for n_1, n_2 in zip(dims[:-1], dims[1:]): + l = gin_mlp_factory(gin_mlp_type, n_1, n_2) + self.convs.append(GINConv(l, train_eps=True)) + self.bns.append(nn.BatchNorm1d(n_2)) + + self.fc = nn.Sequential( + nn.Linear(sum(dims), dim), + # nn.Linear(dims[-1], dim), + nn.BatchNorm1d(dim), + nn.LeakyReLU(), + nn.Linear(dim, 1), + nn.Sigmoid() + ) + + if dim_input==0: + raise Exception("Cannot have neither degree nor node features") + elif 0 in dims: + raise Exception("Cannot have 0 dim hidden layers") + + def reset_parameters(self): + for conv in self.convs: + conv.reset_parameters() + self.fc.apply(weight_reset) + + def forward(self, batch): + node_deg = batch.node_deg + if hasattr(batch, "node_lab"): + node_lab = batch.node_lab + else: + node_lab = None + edge_index = batch.edge_index + if self.use_raw_node_label: + if self.embed_deg is not None and self.fc is not None: + # tmp = [self.embed_deg(node_deg),self.node_label_embedder(batch.x.to(torch.float))] + tmp = [self.embed_deg(node_deg), batch.x] # self.node_label_embedder(batch.x.to(torch.float))] + else: + tmp = [batch.x] + else: + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + tmp = torch.cat(tmp, dim=1) + + z = [tmp] + + for conv, bn in zip(self.convs, self.bns): + x = conv(z[-1], edge_index) + x = bn(x) + x = self.act(x) + z.append(x) + + x = torch.cat(z, dim=1) + ret = self.fc(x).squeeze() + return ret + + +class StandardPershomReadout(nn.Module): + def __init__(self, + dataset, + num_struct_elements=None, + cls_hidden_dimension=None, + drop_out=None, + ): + super().__init__() + assert isinstance(num_struct_elements, int) + # self.use_as_feature_extractor = True#False + + self.ldgm_0 = SLayerRationalHat(num_struct_elements, 2, radius_init=0.1) + self.ldgm_0_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + self.ldgm_1_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + fc_in_feat = num_struct_elements # 3*num_struct_elements + + self.cls_head = ClassifierHead( + dataset, + dim_in=fc_in_feat, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + def forward(self, h_0, h_0_ess, h_1_ess): + tmp = [] + + tmp.append(self.ldgm_0(h_0)) + tmp.append(self.ldgm_0_ess(h_0_ess)) + tmp.append(self.ldgm_1_ess(h_1_ess)) + x = torch.cat(tmp, dim=1) + return x + + +class PershomReadout(nn.Module): + def __init__(self, + dataset, + num_struct_elements=None, + cls_hidden_dimension=None, + drop_out=None, + ): + super().__init__() + assert isinstance(num_struct_elements, int) + # self.use_as_feature_extractor = True#False + + self.ldgm_0_up = SLayerRationalHat(num_struct_elements, 2, radius_init=0.1) + self.ldgm_0_down = SLayerRationalHat(num_struct_elements, 2, radius_init=0.1) + self.ldgm_cc = SLayerRationalHat(num_struct_elements, 2, + radius_init=0.1) # SLayerExponential(num_struct_elements, 2)#SLayerSquare(num_struct_elements, 2) + self.ldgm_h1 = SLayerRationalHat(num_struct_elements, 2, + radius_init=0.1) # SLayerExponential(num_struct_elements, 2)#SLayerSquare(num_struct_elements, 2) + + self.ldgm_0_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + self.ldgm_1_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + fc_in_feat = num_struct_elements # 3*num_struct_elements + + self.cls_head = ClassifierHead( + dataset, + dim_in=fc_in_feat, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + def forward(self, h_0_up, h_0_down, h_0_cc, h_1): + tmp = [] + + tmp.append(self.ldgm_0_up(h_0_up)) + tmp.append(self.ldgm_0_down(h_0_down)) + tmp.append(self.ldgm_cc(h_0_cc)) + tmp.append(self.ldgm_h1(h_1)) + + x = torch.cat(tmp, dim=1) + return x + + +class PershomClassifier(nn.Module): + def __init__(self, + dataset, + fc_in_feat=None, + cls_hidden_dimension=None, + drop_out=None, + ): + super().__init__() + # assert isinstance(num_struct_elements, int) + self.use_as_feature_extractor = False + self.cls_head = ClassifierHead( + dataset, + dim_in=fc_in_feat, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + def forward(self, x): + if not self.use_as_feature_extractor: + x = self.cls_head(x) + + return x + + +class PershomBase(nn.Module): + def __init__(self, aug): + super().__init__() + self.readout_type = "extph" + self.supervised = True + self.use_super_level_set_filtration = None + self.use_as_feature_extractor = False + self.cls = None + self.augmentor = aug + + self.convs = torch.nn.ModuleList() + self.mlp = None + self.classifier_gnn = None + self.classifier_extpers = None + self.classifier_standardpers = None + self.repr2structelems = None + self.structelems2repr = None + self.standard_ph_readout = None + self.epochs = 1 + self.p = 0.01 + self.gnn_node = None + + def compute_extended_ph_link_tree(self, node_filt, batch, device): + ph_input = [] + for idx, (i, j, e) in enumerate(zip(batch.sample_pos[:-1], batch.sample_pos[1:], batch.boundary_info)): + v = node_filt[i:j] # extract vertex values\ + v.to("cpu") + e.to("cpu") + # use this for visualization + if idx == 0 and len(batch.boundary_info) == 1: + # print("v: ",v) + # print("edge_index: ", batch.edge_index) + # print("x: ", batch.x) + pass + ph_input.append((v, [e])) + pers = ph_extended_link_tree(ph_input) # ph_input needs to be: (v,[e]) + + h_0_up = [torch.stack([x.to(device) for x in g[0]]) for g in pers] + h_0_down = [torch.stack([x.to(device) for x in g[1]]) for g in pers] + h_0_extplus = [torch.stack([x.to(device) for x in g[2]]) for g in pers] + h_1_extminus = [torch.stack([x.to(device) for x in g[3]]) for g in pers] + + return h_0_up, h_0_down, h_0_extplus, h_1_extminus + + def forward(self, batch, device): + assert self.use_super_level_set_filtration is not None + if batch.x is not None: + idx = torch.empty((batch.x.size(1),), dtype=torch.float32).uniform_(0, 1) < self.p + batch.x[:, idx] = 0 + node_filt0 = self.fil(batch) + ##h= self.gnn(batch, device) + if self.readout_type == "extph": + h_0_up_1, h_0_down_1, h_0_cc_1, h_1_1 = self.compute_extended_ph_link_tree(node_filt0, batch, device) + g1 = self.readout(h_0_up_1, h_0_down_1, h_0_cc_1, h_1_1) + elif self.readout_type == "sum": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_add_pool(node_filt0, batch.batch) + elif self.readout_type == "max": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_max_pool(node_filt0, batch.batch) + elif self.readout_type == "average": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_mean_pool(node_filt0, batch.batch) + elif self.readout_type == "sort": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_sort_pool(node_filt0, batch.batch, 100) + out = g1 + out = self.classifier_gnn(out) + out = torch.nn.LogSoftmax(dim=1)(out) + return out + + @property + def feature_dimension(self): + return self.cls.cls_head[0].in_features + + @property + def use_as_feature_extractor(self): + return self.use_as_feature_extractor + + @use_as_feature_extractor.setter + def use_as_feature_extractor(self, val): + if hasattr(self, 'cls'): + self.cls.use_as_feature_extractor = val + + def init_weights(self): + def init(m): + if isinstance(m, nn.Linear): + torch.nn.init.xavier_uniform_(m.weight) + # m.bias.data.fill_(0.01) + + self.apply(init) + + +class ClassicReadoutFilt(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool = None, + use_node_degree: bool = None, + set_node_degree_uninformative: bool = None, + use_node_label: bool = None, + use_raw_node_label: bool = None, + filt_conv_number: int = None, + filt_conv_dimension: int = None, + gin_mlp_type: str = None, + num_struct_elements: int = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + conv_number: int = None, + conv_dimension: int = None, + augmentor=(None, None), + readout="sum", + **kwargs, + ): + super().__init__(augmentor) + self.readout_type = readout + + self.use_super_level_set_filtration = use_super_level_set_filtration + + # self.fil = Filtration( + # dataset, + # use_node_degree=use_node_degree, + # set_node_degree_uninformative=set_node_degree_uninformative, + # use_node_label=use_node_label, + # use_raw_node_label=use_raw_node_label, + # filt_conv_number=filt_conv_number, + # filt_conv_dimension=filt_conv_dimension, + # gin_mlp_type=gin_mlp_type, + # ) + + self.fil = MolFiltration( + dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + filt_conv_number=filt_conv_number, + filt_conv_dimension=filt_conv_dimension, + gin_mlp_type=gin_mlp_type, + ) + + self.standard_ph_readout = StandardPershomReadout( + dataset, + num_struct_elements=num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + + self.cls = PershomClassifier(dataset, + fc_in_feat=num_struct_elements, # 3*num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.gnn = ClassicGNN(dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + gin_number=conv_number, + conv_type='GIN', + gin_dimension=conv_dimension, + gin_mlp_type=gin_mlp_type, + ) + self.supervised = True + if self.readout_type == "sort": + self.classifier_gnn = ClassifierHead(dataset, 100, cls_hidden_dimension, drop_out=drop_out) + else: + self.classifier_gnn = ClassifierHead(dataset, 1, cls_hidden_dimension, drop_out=drop_out) + self.init_weights() + + def reset_parameters(self): + self.fil.reset_parameters() + self.classifier_gnn.apply(weight_reset) + + +class PershomLearnedFilt(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool = None, + use_node_degree: bool = None, + set_node_degree_uninformative: bool = None, + use_node_label: bool = None, + use_raw_node_label: bool = None, + gin_number: int = None, + gin_dimension: int = None, + gin_mlp_type: str = None, + num_struct_elements: int = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + augmentor=(None, None), + readout="extph", + **kwargs, + ): + super().__init__(augmentor) + + self.readout_type = "extph" + self.use_super_level_set_filtration = use_super_level_set_filtration + + self.fil = Filtration( + dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + gin_number=gin_number, + gin_dimension=gin_dimension, + gin_mlp_type=gin_mlp_type, + ) + + self.readout = PershomReadout( + dataset, + num_struct_elements=num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.readout.use_as_feature_extractor = True + self.cls = PershomClassifier(dataset, + fc_in_feat=num_struct_elements, # 3*num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.gnn = ClassicGNN(dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + gin_number=gin_number, + gin_dimension=gin_dimension, + gin_mlp_type=gin_mlp_type, + ) + + self.init_weights() + self.supervised = False + + +class PershomLearnedFiltSup(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool = None, + use_node_degree: bool = None, + set_node_degree_uninformative: bool = None, + use_node_label: bool = None, + use_raw_node_label: bool = None, + filt_conv_number: int = None, + filt_conv_dimension: int = None, + gin_mlp_type: str = None, + num_struct_elements: int = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + conv_number: int = None, + conv_dimension: int = None, + augmentor=(None, None), + readout="extph", + **kwargs, + ): + super().__init__(augmentor) + self.use_super_level_set_filtration = use_super_level_set_filtration + + # self.fil = Filtration( + # dataset, + # use_node_degree=use_node_degree, + # set_node_degree_uninformative=set_node_degree_uninformative, + # use_node_label=use_node_label, + # use_raw_node_label=use_raw_node_label, + # filt_conv_number=filt_conv_number, + # filt_conv_dimension=filt_conv_dimension, + # gin_mlp_type=gin_mlp_type, + # ) + self.fil= MolFiltration( + dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + filt_conv_number=filt_conv_number, + filt_conv_dimension=filt_conv_dimension, + gin_mlp_type=gin_mlp_type, + ) + + self.standard_ph_readout = StandardPershomReadout( + dataset, + num_struct_elements=num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + + self.readout = PershomReadout( + dataset, + num_struct_elements=num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.readout.use_as_feature_extractor = True + self.cls = PershomClassifier(dataset, + fc_in_feat=num_struct_elements, # 3*num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.gnn = ClassicGNN(dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + gin_number=conv_number, + conv_type='GIN', + gin_dimension=conv_dimension, + gin_mlp_type=gin_mlp_type, + ) + # self.mlp= nn.Linear(num_struct_elements*4,gin_dimension) + self.classifier_gnn = ClassifierHead(dataset, 4 * num_struct_elements, cls_hidden_dimension, drop_out=drop_out) + self.classifier_extpers = ClassifierHead(dataset, 4 * num_struct_elements, int(cls_hidden_dimension), + drop_out=drop_out) + self.classifier_standardpers = ClassifierHead(dataset, 3 * num_struct_elements, int(cls_hidden_dimension), + drop_out=drop_out) + + self.supervised = True + self.repr2structelems = nn.Linear(conv_dimension, 4 * num_struct_elements) + self.structelems2repr = nn.Linear(4 * num_struct_elements, conv_dimension) + self.init_weights() + + def reset_parameters(self): + self.fil.reset_parameters() + self.classifier_gnn.apply(weight_reset) + + +class PershomRigidDegreeFilt(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool = None, + num_struct_elements: int = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + **kwargs, + ): + super().__init__() + + self.use_super_level_set_filtration = use_super_level_set_filtration + + self.fil = DegreeOnlyFiltration() + + self.cls = PershomClassifier( + dataset, + drop_out=drop_out, + cls_hidden_dimension=cls_hidden_dimension + ) + + self.init_weights() + self.supervised = False + + +class OneHotEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + eye = torch.eye(dim, dtype=torch.float) + + self.register_buffer('eye', eye) + + def forward(self, batch): + assert batch.dtype == torch.long + + return self.eye.index_select(0, batch) + + @property + def dim(self): + return self.eye.size(1) + + +class UniformativeDummyEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + b = torch.ones(1, dim, dtype=torch.float) + self.register_buffer('ones', b) + + def forward(self, batch): + assert batch.dtype == torch.long + return self.ones.expand(batch.size(0), -1) + + @property + def dim(self): + return self.ones.size(1) + + +class GIN(nn.Module): + def __init__(self, + dataset, + use_node_degree: bool = None, + use_node_label: bool = None, + gin_number: int = None, + gin_dimension: int = None, + gin_mlp_type: str = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + set_node_degree_uninformative: bool = None, + pooling_strategy: str = None, + **kwargs, + ): + super().__init__() + self.use_as_feature_extractor = False + self.pooling_strategy = pooling_strategy + self.gin_dimension = gin_dimension + + dim = gin_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if set_node_degree_uninformative and use_node_degree: + self.embed_deg = UniformativeDummyEmbedding(gin_dimension) + elif use_node_degree: + self.embed_deg = OneHotEmbedding(max_node_deg + 1) + else: + self.embed_deg = None + + self.embed_lab = OneHotEmbedding(num_node_lab) if use_node_label else None + + dim_input = 0 + dim_input += self.embed_deg.dim if use_node_degree else 0 + dim_input += self.embed_lab.dim if use_node_label else 0 + assert dim_input > 0 + + dims = [dim_input] + (gin_number) * [dim] + self.convs = nn.ModuleList() + self.bns = nn.ModuleList() + self.act = torch.nn.functional.leaky_relu + + for n_1, n_2 in zip(dims[:-1], dims[1:]): + l = gin_mlp_factory(gin_mlp_type, n_1, n_2) + self.convs.append(GINConv(l, train_eps=True)) + self.bns.append(nn.BatchNorm1d(n_2)) + + if pooling_strategy == 'sum': + self.global_pool_fn = global_add_pool + elif pooling_strategy == 'sort': + self.k = int(np.percentile([d.num_nodes for d in dataset], 10)) + self.global_pool_fn = functools.partial(global_sort_pool, k=self.k) + self.sort_pool_nn = nn.Linear(self.k * gin_dimension, gin_dimension) + else: + raise ValueError + + self.cls = ClassifierHead( + dataset, + dim_in=gin_dimension, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + @property + def feature_dimension(self): + return self.cls.cls_head[0].in_features + + def forward(self, batch): + + node_deg = batch.node_deg + node_lab = batch.node_lab + + edge_index = batch.edge_index + + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + tmp = torch.cat(tmp, dim=1) + + z = [tmp] + + for conv, bn in zip(self.convs, self.bns): + x = conv(z[-1], edge_index) + x = bn(x) + x = self.act(x) + z.append(x) + + x = z[-1] + x = self.global_pool_fn(x, batch.batch) + + if self.pooling_strategy == 'sort': + x = self.sort_pool_nn(x) + x = x.squeeze() + + if not self.use_as_feature_extractor: + x = self.cls(x) + + return x + + +class SimpleNNBaseline(nn.Module): + def __init__(self, + dataset, + use_node_degree: bool = None, + use_node_label: bool = None, + set_node_degree_uninformative: bool = None, + gin_dimension: int = None, + gin_mlp_type: str = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + pooling_strategy: str = None, + **kwargs, + ): + super().__init__() + self.use_as_feature_extractor = False + self.pooling_strategy = pooling_strategy + self.gin_dimension = gin_dimension + + dim = gin_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if set_node_degree_uninformative and use_node_degree: + self.embed_deg = UniformativeDummyEmbedding(gin_dimension) + elif use_node_degree: + self.embed_deg = OneHotEmbedding(max_node_deg + 1) + else: + self.embed_deg = None + + self.embed_lab = OneHotEmbedding(num_node_lab) if use_node_label else None + + dim_input = 0 + dim_input += self.embed_deg.dim if use_node_degree else 0 + dim_input += self.embed_lab.dim if use_node_label else 0 + assert dim_input > 0 + + self.mlp = gin_mlp_factory(gin_mlp_type, dim_input, dim) + + if pooling_strategy == 'sum': + self.global_pool_fn = global_add_pool + elif pooling_strategy == 'sort': + self.k = int(np.percentile([d.num_nodes for d in dataset], 10)) + self.global_pool_fn = functools.partial(global_sort_pool, k=self.k) + self.sort_pool_nn = nn.Linear(self.k * gin_dimension, gin_dimension) + else: + raise ValueError + + self.cls = ClassifierHead( + dataset, + dim_in=gin_dimension, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + @property + def feature_dimension(self): + return self.cls.cls_head[0].in_features + + def forward(self, batch): + + node_deg = batch.node_deg + node_lab = batch.node_lab + + edge_index = batch.edge_index + + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + x = torch.cat(tmp, dim=1) + + x = self.mlp(x) + x = self.global_pool_fn(x, batch.batch) + + if self.pooling_strategy == 'sort': + x = self.sort_pool_nn(x) + x = x.squeeze() + + if not self.use_as_feature_extractor: + x = self.cls(x) + + return x + diff --git a/gnn/mutag_model.py b/gnn/mutag_model.py new file mode 100644 index 0000000..02849d6 --- /dev/null +++ b/gnn/mutag_model.py @@ -0,0 +1,910 @@ +import torch +import numpy as np +import torch.nn as nn + +import functools + +from torchPHext.torchex_PHext.nn import SLayerRationalHat, SLayerSquare, SLayerExponential +from torch_geometric.nn import SAGEConv, LEConv, GINConv, global_add_pool, global_sort_pool, global_mean_pool, \ + global_max_pool +from torchPHext.torchex_PHext import pershom as pershom_ext +from torch_geometric.nn import GINConv, global_add_pool +import sys + +sys.path.append(".") + +ph_extended_link_tree = pershom_ext.pershom_backend.__C.VertExtendedFiltCompCuda_link_cut_tree__extended_persistence_batch + + +def gin_mlp_factory(gin_mlp_type: str, dim_in: int, dim_out: int): + if gin_mlp_type == 'lin': + return nn.Linear(dim_in, dim_out) + + elif gin_mlp_type == 'lin_lrelu_lin': + return nn.Sequential( + nn.Linear(dim_in, dim_in), + nn.LeakyReLU(), + nn.Linear(dim_in, dim_out) + ) + + elif gin_mlp_type == 'lin_bn_lrelu_lin': + return nn.Sequential( + nn.Linear(dim_in, dim_in), + nn.BatchNorm1d(dim_in), + nn.LeakyReLU(), + nn.Linear(dim_in, dim_out) + ) + else: + raise ValueError("Unknown gin_mlp_type!") + + +def ClassifierHead( + dataset, + dim_in: int = None, + hidden_dim: int = None, + drop_out: float = None): + assert (0.0 <= drop_out) and (drop_out < 1.0) + assert dim_in is not None + assert drop_out is not None + assert hidden_dim is not None + + tmp = [ + nn.Linear(dim_in, hidden_dim), + nn.LeakyReLU(), + ] + + if drop_out > 0: + tmp += [nn.Dropout(p=drop_out)] + + tmp += [nn.Linear(hidden_dim, dataset.num_classes)] + + return nn.Sequential(*tmp) + + +class DegreeOnlyFiltration(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, batch): + tmp = [] + for i, j in zip(batch.sample_pos[:-1], batch.sample_pos[1:]): + max_deg = batch.node_deg[i:j].max() + + t = torch.ones(j - i, dtype=torch.float, device=batch.node_deg.device) + t = t * max_deg + tmp.append(t) + + max_deg = torch.cat(tmp, dim=0) + + normalized_node_deg = batch.node_deg.float() / max_deg + + return normalized_node_deg + +class ClassicGNN(torch.nn.Module): + def __init__(self, + dataset, + use_node_degree=None, + set_node_degree_uninformative=None, + use_node_label=None, + use_raw_node_label=None, + gin_number=None, + gin_dimension=None, + conv_type='GIN', + gin_mlp_type=None, + cls_hidden_dimension=512, + drop_out=0.5, + **kwargs + ): + super().__init__() + + dim = gin_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if use_node_degree: + self.embed_deg = nn.Embedding(max_node_deg + 1, dim) + else: + self.embed_deg = None + + self.embed_lab = nn.Embedding(num_node_lab, dim) if use_node_label else None + self.use_raw_node_label = use_raw_node_label + + dim_input = dim*((self.embed_deg is not None) + (self.embed_lab is not None)) + # dim_input = dim * ((self.embed_deg is not None)) + if (use_raw_node_label): + dim_input += dataset[0].x.size(1) + + dims = [dim_input] + (gin_number) * [dim] + + self.convs = nn.ModuleList() + self.bns = nn.ModuleList() + self.act = torch.nn.functional.leaky_relu + if use_raw_node_label: + self.node_label_embedder = torch.nn.Linear(num_node_lab, gin_dimension) + + for n_1, n_2 in zip(dims[:-1], dims[1:]): + if conv_type == 'GIN': + l = gin_mlp_factory(gin_mlp_type, n_1, n_2) + self.convs.append(GINConv(l, train_eps=True)) + # self.convs.append(LEConv(n_1,n_2)) + elif conv_type == 'GraphSAGE': + self.convs.append(SAGEConv(n_1, n_2)) + + self.bns.append(nn.BatchNorm1d(n_2)) + self.fc = None + self.classifier_extpers = ClassifierHead(dataset, gin_dimension, int(cls_hidden_dimension), + drop_out=drop_out) + + def forward2(self, h, batch): + h = global_add_pool(h, batch.batch) + out = self.classifier_extpers(h) + out = torch.nn.LogSoftmax(dim=1)(out) + return out + + def forward(self, batch, device): + + node_deg = batch.node_deg + if hasattr(batch, "node_lab"): + node_lab = batch.node_lab + else: + node_lab = None + edge_index = batch.edge_index + tmp= [] + if self.use_raw_node_label: + if self.embed_deg is not None: + # tmp = [self.embed_deg(node_deg),self.node_label_embedder(node_lab.to(torch.float))] + tmp = [self.embed_deg(node_deg), batch.x] + else: + tmp = [batch.x] + else: + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + if len(tmp) > 0: + tmp = torch.cat(tmp, dim=1) + else: + tmp = torch.tensor(tmp) + x = tmp + for conv, bn in zip(self.convs[:-1], self.bns[:-1]): + x = conv(x, edge_index) + x = bn(x) + x = self.act(x) + x = torch.nn.Dropout(p=0.5)(x) + # z.append(x) + x = self.convs[-1](x, edge_index) + + return x + + +def weight_reset(m): + if isinstance(m, nn.Linear): + m.reset_parameters() + + +class Filtration(torch.nn.Module): + def __init__(self, + dataset, + use_node_degree=None, + set_node_degree_uninformative=None, + use_node_label=None, + use_raw_node_label=None, + filt_conv_number=None, + filt_conv_dimension=None, + gin_mlp_type=None, + **kwargs + ): + super().__init__() + + dim = filt_conv_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if use_node_degree: + self.embed_deg = nn.Embedding(max_node_deg + 1, dim) + else: + self.embed_deg = None + + self.embed_lab = nn.Embedding(num_node_lab, dim) if (use_node_label and not use_raw_node_label and not (num_node_lab==0 or num_node_lab is None)) else None + self.use_raw_node_label = use_raw_node_label + + dim_input = dim*((self.embed_deg is not None) + (self.embed_lab is not None)) + + if (use_raw_node_label) and dataset[0].x is not None: + dim_input += dataset[0].x.size(1) + + dims = [dim_input] + (filt_conv_number) * [dim] + + self.convs = nn.ModuleList() + self.bns = nn.ModuleList() + self.act = torch.nn.functional.leaky_relu + + for n_1, n_2 in zip(dims[:-1], dims[1:]): + l = gin_mlp_factory(gin_mlp_type, n_1, n_2) + self.convs.append(GINConv(l, train_eps=True)) + self.bns.append(nn.BatchNorm1d(n_2)) + + self.fc = nn.Sequential( + nn.Linear(sum(dims), dim), + # nn.Linear(dims[-1], dim), + nn.BatchNorm1d(dim), + nn.LeakyReLU(), + nn.Linear(dim, 1), + nn.Sigmoid() + ) + + if 0 == dim_input: + raise Exception("Cannot have neither degree nor node features") + elif 0 in dims: + raise Exception("Cannot have 0 dim hidden layers") + + def reset_parameters(self): + for conv in self.convs: + conv.reset_parameters() + self.fc.apply(weight_reset) + + def forward(self, batch): + node_deg = batch.node_deg + if hasattr(batch, "node_lab"): + node_lab = batch.node_lab + else: + node_lab = None + edge_index = batch.edge_index + if self.use_raw_node_label and batch.x is not None: + if self.embed_deg is not None and self.fc is not None: + # tmp = [self.embed_deg(node_deg),self.node_label_embedder(batch.x.to(torch.float))] + tmp = [self.embed_deg(node_deg), batch.x] # self.node_label_embedder(batch.x.to(torch.float))] + else: + tmp = [batch.x] + else: + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + tmp = torch.cat(tmp, dim=1) + + z = [tmp] + + for conv, bn in zip(self.convs, self.bns): + x = conv(z[-1], edge_index) + x = bn(x) + x = self.act(x) + z.append(x) + + x = torch.cat(z, dim=1) + ret = self.fc(x).squeeze() + return ret + + +class StandardPershomReadout(nn.Module): + def __init__(self, + dataset, + num_struct_elements=None, + cls_hidden_dimension=None, + drop_out=None, + ): + super().__init__() + assert isinstance(num_struct_elements, int) + # self.use_as_feature_extractor = True#False + + self.ldgm_0 = SLayerRationalHat(num_struct_elements, 2, radius_init=0.1) + self.ldgm_0_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + self.ldgm_1_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + fc_in_feat = num_struct_elements # 3*num_struct_elements + + self.cls_head = ClassifierHead( + dataset, + dim_in=fc_in_feat, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + def forward(self, h_0, h_0_ess, h_1_ess): + tmp = [] + + tmp.append(self.ldgm_0(h_0)) + tmp.append(self.ldgm_0_ess(h_0_ess)) + tmp.append(self.ldgm_1_ess(h_1_ess)) + x = torch.cat(tmp, dim=1) + return x + + +class PershomReadout(nn.Module): + def __init__(self, + dataset, + num_struct_elements=None, + cls_hidden_dimension=None, + drop_out=None, + ): + super().__init__() + assert isinstance(num_struct_elements, int) + # self.use_as_feature_extractor = True#False + + self.ldgm_0_up = SLayerRationalHat(num_struct_elements, 2, radius_init=0.1) + self.ldgm_0_down = SLayerRationalHat(num_struct_elements, 2, radius_init=0.1) + self.ldgm_cc = SLayerRationalHat(num_struct_elements, 2, + radius_init=0.1) # SLayerExponential(num_struct_elements, 2)#SLayerSquare(num_struct_elements, 2) + self.ldgm_h1 = SLayerRationalHat(num_struct_elements, 2, + radius_init=0.1) # SLayerExponential(num_struct_elements, 2)#SLayerSquare(num_struct_elements, 2) + + self.ldgm_0_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + self.ldgm_1_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + fc_in_feat = num_struct_elements # 3*num_struct_elements + + self.cls_head = ClassifierHead( + dataset, + dim_in=fc_in_feat, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + def forward(self, h_0_up, h_0_down, h_0_cc, h_1): + tmp = [] + + tmp.append(self.ldgm_0_up(h_0_up)) + tmp.append(self.ldgm_0_down(h_0_down)) + # TODO: ablation + tmp.append(self.ldgm_cc(h_0_cc)) + tmp.append(self.ldgm_h1(h_1)) + + x = torch.cat(tmp, dim=1) + return x + + +class PershomClassifier(nn.Module): + def __init__(self, + dataset, + fc_in_feat=None, + cls_hidden_dimension=None, + drop_out=None, + ): + super().__init__() + # assert isinstance(num_struct_elements, int) + self.use_as_feature_extractor = False + self.cls_head = ClassifierHead( + dataset, + dim_in=fc_in_feat, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + def forward(self, x): + if not self.use_as_feature_extractor: + x = self.cls_head(x) + + return x + + +class PershomBase(nn.Module): + def __init__(self, aug): + super().__init__() + self.readout_type = "extph" + self.supervised = True + self.use_super_level_set_filtration = None + self.use_as_feature_extractor = False + self.cls = None + self.augmentor = aug + self.convs = torch.nn.ModuleList() + self.mlp = None + self.classifier_gnn = None + self.classifier_extpers = None + self.classifier_standardpers = None + self.repr2structelems = None + self.structelems2repr = None + self.standard_ph_readout = None + self.epochs = 1 + self.p = 0.01 + self.gnn_node = None + + def compute_ph(self, node_filt, batch): + ph_input = [] + for i, j, e in zip(batch.sample_pos[:-1], batch.sample_pos[1:], batch.boundary_info): + v = node_filt[i:j] + + ph_input.append((v, [e])) + + pers = ph(ph_input) + + if not self.use_super_level_set_filtration: + h_0 = [x[0][0] for x in pers] + h_0_ess = [x[1][0].unsqueeze(1) for x in pers] + h_1_ess = [x[1][1].unsqueeze(1) for x in pers] + + else: + ph_sup_input = [(-v, e) for v, e in ph_input] + pers_sup = ph(ph_sup_input) + + h_0 = [torch.cat([x[0][0], -(y[0][0])], dim=0) for x, y in zip(pers, pers_sup)] + h_0_ess = [torch.cat([x[1][0], -(y[1][0])], dim=0).unsqueeze(1) for x, y in zip(pers, pers_sup)] + h_1_ess = [torch.cat([x[1][1], -(y[1][1])], dim=0).unsqueeze(1) for x, y in zip(pers, pers_sup)] + return h_0, h_0_ess, h_1_ess + + def compute_extended_ph_link_tree(self, node_filt, batch, device): + ph_input = [] + for idx, (i, j, e) in enumerate(zip(batch.sample_pos[:-1], batch.sample_pos[1:], batch.boundary_info)): + v = node_filt[i:j] # extract vertex values\ + v.to("cpu") + e.to("cpu") + # use this for visualization + if idx == 0 and len(batch.boundary_info) == 1: + # print("v: ",v) + # print("edge_index: ", batch.edge_index) + # print("x: ", batch.x) + pass + ph_input.append((v, [e])) + pers = ph_extended_link_tree(ph_input) # ph_input needs to be: (v,[e]) + + h_0_up = [torch.stack([x.to(device) for x in g[0]]) for g in pers] + h_0_down = [torch.stack([x.to(device) for x in g[1]]) for g in pers] + h_0_extplus = [torch.stack([x.to(device) for x in g[2]]) for g in pers] + h_1_extminus = [torch.stack([x.to(device) for x in g[3]]) for g in pers] + + return h_0_up, h_0_down, h_0_extplus, h_1_extminus + + def forward(self, batch, device): + if batch.x is not None: + idx = torch.empty((batch.x.size(1),), dtype=torch.float32).uniform_(0, 1) < self.p + batch.x[:, idx] = 0 + node_filt0 = self.fil(batch) + ##h= self.gnn(batch, device) + if self.readout_type == "extph": + h_0_up_1, h_0_down_1, h_0_cc_1, h_1_1 = self.compute_extended_ph_link_tree(node_filt0, batch, device) + g1 = self.readout(h_0_up_1, h_0_down_1, h_0_cc_1, h_1_1) + elif self.readout_type == "sum": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_add_pool(node_filt0, batch.batch) + elif self.readout_type == "max": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_max_pool(node_filt0, batch.batch) + elif self.readout_type == "average": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_mean_pool(node_filt0, batch.batch) + elif self.readout_type == "sort": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_sort_pool(node_filt0, batch.batch, 400) + out = g1 + out = self.classifier_gnn(out) + out = torch.nn.LogSoftmax(dim=1)(out) + return out + + @property + def feature_dimension(self): + return self.cls.cls_head[0].in_features + + @property + def use_as_feature_extractor(self): + return self.use_as_feature_extractor + + @use_as_feature_extractor.setter + def use_as_feature_extractor(self, val): + if hasattr(self, 'cls'): + self.cls.use_as_feature_extractor = val + + def init_weights(self): + def init(m): + if isinstance(m, nn.Linear): + torch.nn.init.xavier_uniform_(m.weight) + # m.bias.data.fill_(0.01) + + self.apply(init) + + +class ClassicReadoutFilt(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool = None, + use_node_degree: bool = None, + set_node_degree_uninformative: bool = None, + use_node_label: bool = None, + use_raw_node_label: bool = None, + filt_conv_number: int = None, + filt_conv_dimension: int = None, + gin_mlp_type: str = None, + num_struct_elements: int = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + conv_number: int = None, + conv_dimension: int = None, + augmentor=(None, None), + readout="sum", + **kwargs, + ): + super().__init__(augmentor) + self.readout_type = readout + + self.use_super_level_set_filtration = use_super_level_set_filtration + + self.fil = Filtration( + dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + filt_conv_number=filt_conv_number, + filt_conv_dimension=filt_conv_dimension, + gin_mlp_type=gin_mlp_type, + ) + + self.cls = PershomClassifier(dataset, + fc_in_feat=num_struct_elements, # 3*num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.gnn = ClassicGNN(dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + gin_number=conv_number, + conv_type='GIN', + gin_dimension=conv_dimension, + gin_mlp_type=gin_mlp_type, + ) + self.supervised = True + if self.readout_type == "sort": + self.classifier_gnn = ClassifierHead(dataset, 400, cls_hidden_dimension, drop_out=drop_out) + else: + self.classifier_gnn = ClassifierHead(dataset, 1, cls_hidden_dimension, drop_out=drop_out) + self.init_weights() + + def reset_parameters(self): + self.fil.reset_parameters() + self.classifier_gnn.apply(weight_reset) + + +class PershomLearnedFilt(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool = None, + use_node_degree: bool = None, + set_node_degree_uninformative: bool = None, + use_node_label: bool = None, + use_raw_node_label: bool = None, + gin_number: int = None, + gin_dimension: int = None, + gin_mlp_type: str = None, + num_struct_elements: int = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + augmentor=(None, None), + readout="extph", + **kwargs, + ): + super().__init__(augmentor) + + self.readout_type = "extph" + self.use_super_level_set_filtration = use_super_level_set_filtration + + self.fil = Filtration( + dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + gin_number=gin_number, + gin_dimension=gin_dimension, + gin_mlp_type=gin_mlp_type, + ) + + self.readout = PershomReadout( + dataset, + num_struct_elements=num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.readout.use_as_feature_extractor = True + self.cls = PershomClassifier(dataset, + fc_in_feat=num_struct_elements, # 3*num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.gnn = ClassicGNN(dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + gin_number=gin_number, + gin_dimension=gin_dimension, + gin_mlp_type=gin_mlp_type, + ) + + self.init_weights() + self.supervised = False + + +class PershomLearnedFiltSup(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool = None, + use_node_degree: bool = None, + set_node_degree_uninformative: bool = None, + use_node_label: bool = None, + use_raw_node_label: bool = None, + filt_conv_number: int = None, + filt_conv_dimension: int = None, + gin_mlp_type: str = None, + num_struct_elements: int = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + conv_number: int = None, + conv_dimension: int = None, + augmentor=(None, None), + readout="extph", + **kwargs, + ): + super().__init__(augmentor) + + self.use_super_level_set_filtration = use_super_level_set_filtration + + self.fil = Filtration( + dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + filt_conv_number=filt_conv_number, + filt_conv_dimension=filt_conv_dimension, + gin_mlp_type=gin_mlp_type, + ) + + self.readout = PershomReadout( + dataset, + num_struct_elements=num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.readout.use_as_feature_extractor = True + self.classifier_gnn = ClassifierHead(dataset, 4 * num_struct_elements, cls_hidden_dimension, drop_out=drop_out) + self.supervised = True + self.init_weights() + + def reset_parameters(self): + self.fil.reset_parameters() + self.classifier_gnn.apply(weight_reset) + + +class PershomRigidDegreeFilt(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool = None, + num_struct_elements: int = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + **kwargs, + ): + super().__init__() + + self.use_super_level_set_filtration = use_super_level_set_filtration + + self.fil = DegreeOnlyFiltration() + + self.cls = PershomClassifier( + dataset, + drop_out=drop_out, + cls_hidden_dimension=cls_hidden_dimension + ) + + self.init_weights() + self.supervised = False + + +class OneHotEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + eye = torch.eye(dim, dtype=torch.float) + + self.register_buffer('eye', eye) + + def forward(self, batch): + assert batch.dtype == torch.long + + return self.eye.index_select(0, batch) + + @property + def dim(self): + return self.eye.size(1) + + +class UniformativeDummyEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + b = torch.ones(1, dim, dtype=torch.float) + self.register_buffer('ones', b) + + def forward(self, batch): + assert batch.dtype == torch.long + return self.ones.expand(batch.size(0), -1) + + @property + def dim(self): + return self.ones.size(1) + + +class GIN(nn.Module): + def __init__(self, + dataset, + use_node_degree: bool = None, + use_node_label: bool = None, + gin_number: int = None, + gin_dimension: int = None, + gin_mlp_type: str = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + set_node_degree_uninformative: bool = None, + pooling_strategy: str = None, + **kwargs, + ): + super().__init__() + self.use_as_feature_extractor = False + self.pooling_strategy = pooling_strategy + self.gin_dimension = gin_dimension + + dim = gin_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if set_node_degree_uninformative and use_node_degree: + self.embed_deg = UniformativeDummyEmbedding(gin_dimension) + elif use_node_degree: + self.embed_deg = OneHotEmbedding(max_node_deg + 1) + else: + self.embed_deg = None + + self.embed_lab = OneHotEmbedding(num_node_lab) if use_node_label else None + + dim_input = 0 + dim_input += self.embed_deg.dim if use_node_degree else 0 + dim_input += self.embed_lab.dim if use_node_label else 0 + assert dim_input > 0 + + dims = [dim_input] + (gin_number) * [dim] + self.convs = nn.ModuleList() + self.bns = nn.ModuleList() + self.act = torch.nn.functional.leaky_relu + + for n_1, n_2 in zip(dims[:-1], dims[1:]): + l = gin_mlp_factory(gin_mlp_type, n_1, n_2) + self.convs.append(GINConv(l, train_eps=True)) + self.bns.append(nn.BatchNorm1d(n_2)) + + if pooling_strategy == 'sum': + self.global_pool_fn = global_add_pool + elif pooling_strategy == 'sort': + self.k = int(np.percentile([d.num_nodes for d in dataset], 10)) + self.global_pool_fn = functools.partial(global_sort_pool, k=self.k) + self.sort_pool_nn = nn.Linear(self.k * gin_dimension, gin_dimension) + else: + raise ValueError + + self.cls = ClassifierHead( + dataset, + dim_in=gin_dimension, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + @property + def feature_dimension(self): + return self.cls.cls_head[0].in_features + + def forward(self, batch): + + node_deg = batch.node_deg + node_lab = batch.node_lab + + edge_index = batch.edge_index + + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + tmp = torch.cat(tmp, dim=1) + + z = [tmp] + + for conv, bn in zip(self.convs, self.bns): + x = conv(z[-1], edge_index) + x = bn(x) + x = self.act(x) + z.append(x) + + x = z[-1] + x = self.global_pool_fn(x, batch.batch) + + if self.pooling_strategy == 'sort': + x = self.sort_pool_nn(x) + x = x.squeeze() + + if not self.use_as_feature_extractor: + x = self.cls(x) + + return x + + +class SimpleNNBaseline(nn.Module): + def __init__(self, + dataset, + use_node_degree: bool = None, + use_node_label: bool = None, + set_node_degree_uninformative: bool = None, + gin_dimension: int = None, + gin_mlp_type: str = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + pooling_strategy: str = None, + **kwargs, + ): + super().__init__() + self.use_as_feature_extractor = False + self.pooling_strategy = pooling_strategy + self.gin_dimension = gin_dimension + + dim = gin_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if set_node_degree_uninformative and use_node_degree: + self.embed_deg = UniformativeDummyEmbedding(gin_dimension) + elif use_node_degree: + self.embed_deg = OneHotEmbedding(max_node_deg + 1) + else: + self.embed_deg = None + + self.embed_lab = OneHotEmbedding(num_node_lab) if use_node_label else None + + dim_input = 0 + dim_input += self.embed_deg.dim if use_node_degree else 0 + dim_input += self.embed_lab.dim if use_node_label else 0 + assert dim_input > 0 + + self.mlp = gin_mlp_factory(gin_mlp_type, dim_input, dim) + + if pooling_strategy == 'sum': + self.global_pool_fn = global_add_pool + elif pooling_strategy == 'sort': + self.k = int(np.percentile([d.num_nodes for d in dataset], 10)) + self.global_pool_fn = functools.partial(global_sort_pool, k=self.k) + self.sort_pool_nn = nn.Linear(self.k * gin_dimension, gin_dimension) + else: + raise ValueError + + self.cls = ClassifierHead( + dataset, + dim_in=gin_dimension, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + @property + def feature_dimension(self): + return self.cls.cls_head[0].in_features + + def forward(self, batch): + + node_deg = batch.node_deg + node_lab = batch.node_lab + + edge_index = batch.edge_index + + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + x = torch.cat(tmp, dim=1) + + x = self.mlp(x) + x = self.global_pool_fn(x, batch.batch) + + if self.pooling_strategy == 'sort': + x = self.sort_pool_nn(x) + x = x.squeeze() + + if not self.use_as_feature_extractor: + x = self.cls(x) + + return x + diff --git a/gnn/pinwheels_model.py b/gnn/pinwheels_model.py new file mode 100644 index 0000000..6ce4850 --- /dev/null +++ b/gnn/pinwheels_model.py @@ -0,0 +1,911 @@ +import torch +import numpy as np +import torch.nn as nn + +import functools + +from torchPHext.torchex_PHext.nn import SLayerRationalHat, SLayerSquare, SLayerExponential +from torch_geometric.nn import SAGEConv, LEConv, GINConv, global_add_pool, global_sort_pool, global_max_pool, global_mean_pool +from torchPHext.torchex_PHext import pershom as pershom_ext +from torch_geometric.nn import GINConv, global_add_pool +import sys +sys.path.append(".") + +ph_extended_link_tree = pershom_ext.pershom_backend.__C.VertExtendedFiltCompCuda_link_cut_tree__extended_persistence_batch + +def gin_mlp_factory(gin_mlp_type: str, dim_in: int, dim_out: int): + if gin_mlp_type == 'lin': + return nn.Linear(dim_in, dim_out) + + elif gin_mlp_type == 'lin_lrelu_lin': + return nn.Sequential( + nn.Linear(dim_in, dim_in), + nn.LeakyReLU(), + nn.Linear(dim_in, dim_out) + ) + + elif gin_mlp_type == 'lin_bn_lrelu_lin': + return nn.Sequential( + nn.Linear(dim_in, dim_in), + nn.BatchNorm1d(dim_in), + nn.LeakyReLU(), + nn.Linear(dim_in, dim_out) + ) + else: + raise ValueError("Unknown gin_mlp_type!") + + +def ClassifierHead( + dataset, + dim_in: int=None, + hidden_dim: int=None, + drop_out: float=None): + + assert (0.0 <= drop_out) and (drop_out < 1.0) + assert dim_in is not None + assert drop_out is not None + assert hidden_dim is not None + + tmp = [ + nn.Linear(dim_in, hidden_dim), + nn.LeakyReLU(), + ] + + if drop_out > 0: + tmp += [nn.Dropout(p=drop_out)] + + tmp += [nn.Linear(hidden_dim, dataset.num_classes)] + + return nn.Sequential(*tmp) + + +class DegreeOnlyFiltration(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, batch): + tmp = [] + for i, j in zip(batch.sample_pos[:-1], batch.sample_pos[1:]): + max_deg = batch.node_deg[i:j].max() + + t = torch.ones(j - i, dtype=torch.float, device=batch.node_deg.device) + t = t * max_deg + tmp.append(t) + + max_deg = torch.cat(tmp, dim=0) + + normalized_node_deg = batch.node_deg.float() / max_deg + + return normalized_node_deg + +class ClassicGNN(torch.nn.Module): + def __init__(self, + dataset, + use_node_degree=None, + set_node_degree_uninformative=None, + use_node_label=None, + use_raw_node_label= None, + gin_number=None, + gin_dimension=None, + conv_type= 'GIN', + gin_mlp_type = None, + cls_hidden_dimension= 512, + drop_out= 0.5, + **kwargs + ): + super().__init__() + + dim = gin_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if set_node_degree_uninformative and use_node_degree: + self.embed_deg = UniformativeDummyEmbedding(gin_dimension) + elif use_node_degree: + self.embed_deg = nn.Embedding(max_node_deg+1, dim) + else: + self.embed_deg = None + + self.embed_lab = nn.Embedding(num_node_lab, dim) if use_node_label else None + self.use_raw_node_label= use_raw_node_label + + dim_input = dim*((self.embed_deg is not None) + (self.embed_lab is not None)) + if (use_raw_node_label) and dataset[0].x is not None: + dim_input+= dataset[0].x.size(1) + + dims = [dim_input] + (gin_number)*[dim] + + self.convs = nn.ModuleList() + self.bns = nn.ModuleList() + self.act = torch.nn.functional.leaky_relu + if use_raw_node_label: + self.node_label_embedder= torch.nn.Linear(num_node_lab, gin_dimension) + + for n_1, n_2 in zip(dims[:-1], dims[1:]): + if conv_type=='GIN': + l = gin_mlp_factory(gin_mlp_type, n_1, n_2) + self.convs.append(GINConv(l, train_eps=True)) + elif conv_type== 'GraphSAGE': + self.convs.append(SAGEConv(n_1, n_2)) + + self.bns.append(nn.BatchNorm1d(n_2)) + self.fc= None + self.classifier_extpers = ClassifierHead(dataset, gin_dimension, int(cls_hidden_dimension), + drop_out=drop_out) + def forward2(self, h, batch): + h= global_add_pool(h, batch.batch) + out= self.classifier_extpers(h) + out= torch.nn.LogSoftmax(dim=1)(out) + return out + def forward(self, batch, device): + + node_deg = batch.node_deg + if hasattr(batch, "node_lab"): + node_lab = batch.node_lab + else: + node_lab= None + edge_index = batch.edge_index + if self.use_raw_node_label: + if self.embed_deg is not None: + # tmp = [self.embed_deg(node_deg),self.node_label_embedder(node_lab.to(torch.float))] + tmp = [self.embed_deg(node_deg),batch.x] + else: + tmp= [] + raise ValueError + else: + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + if len(tmp)>0: + tmp = torch.cat(tmp, dim=1) + else: + tmp= torch.tensor(tmp) + x= tmp + for conv, bn in zip(self.convs[:-1], self.bns[:-1]): + x = conv(x, edge_index) + x = bn(x) + x = self.act(x) + x= torch.nn.Dropout(p=0.5)(x) + x= self.convs[-1](x,edge_index) + + return x +def weight_reset(m): + if isinstance(m, nn.Linear): + m.reset_parameters() +class Filtration(torch.nn.Module): + def __init__(self, + dataset, + use_node_degree=None, + set_node_degree_uninformative=None, + use_node_label=None, + use_raw_node_label= None, + filt_conv_number=None, + filt_conv_dimension=None, + gin_mlp_type = None, + **kwargs + ): + super().__init__() + + dim = filt_conv_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if use_node_degree: + self.embed_deg = nn.Embedding(max_node_deg+1, dim) + else: + self.embed_deg = None + + self.embed_lab = nn.Embedding(num_node_lab, dim) if use_node_label else None + self.use_raw_node_label= use_raw_node_label + + dim_input = dim*((self.embed_deg is not None) + (self.embed_lab is not None)) + if (self.embed_lab is not None): + dim_input += dataset[0].x.shape(1) + + dims = [dim_input] + (filt_conv_number)*[dim] + + self.convs = nn.ModuleList() + self.bns = nn.ModuleList() + self.act = torch.nn.functional.leaky_relu + if use_raw_node_label: + self.node_label_embedder= torch.nn.Linear(dataset[0].x.size(1), filt_conv_dimension) + + for n_1, n_2 in zip(dims[:-1], dims[1:]): + l = gin_mlp_factory(gin_mlp_type, n_1, n_2) + self.convs.append(GINConv(l, train_eps=True)) + self.bns.append(nn.BatchNorm1d(n_2)) + + self.fc = nn.Sequential( + nn.Linear(sum(dims), dim), + nn.BatchNorm1d(dim), + nn.LeakyReLU(), + nn.Linear(dim, 1), + nn.Sigmoid() + ) + + if dim_input==0: + raise Exception("Cannot have neither degree nor node features") + elif 0 in dims: + raise Exception("Cannot have 0 dim hidden layers") + + def reset_parameters(self): + for conv in self.convs: + conv.reset_parameters() + self.fc.apply(weight_reset) + + def forward(self, batch): + + node_deg = batch.node_deg + if hasattr(batch, "node_lab"): + node_lab = batch.node_lab + else: + node_lab= None + edge_index = batch.edge_index + if self.use_raw_node_label: + if self.embed_deg is not None and self.fc is not None: + tmp = [self.embed_deg(node_deg),batch.x] + else: + tmp= [batch.x] + else: + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + tmp = torch.cat(tmp, dim=1) + + z = [tmp] + + for conv, bn in zip(self.convs, self.bns): + x = conv(z[-1], edge_index) + x = bn(x) + x = self.act(x) + z.append(x) + + x = torch.cat(z, dim=1) + ret = self.fc(x).squeeze() + return ret + +class StandardPershomReadout(nn.Module): + def __init__(self, + dataset, + num_struct_elements=None, + cls_hidden_dimension=None, + drop_out=None, + ): + + super().__init__() + assert isinstance(num_struct_elements, int) + #self.use_as_feature_extractor = True#False + + self.ldgm_0 = SLayerRationalHat(num_struct_elements, 2, radius_init=0.1) + self.ldgm_0_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + self.ldgm_1_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + fc_in_feat = num_struct_elements#3*num_struct_elements + + self.cls_head = ClassifierHead( + dataset, + dim_in=fc_in_feat, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + def forward(self, h_0,h_0_ess, h_1_ess): + tmp = [] + + tmp.append(self.ldgm_0(h_0)) + tmp.append(self.ldgm_0_ess(h_0_ess)) + tmp.append(self.ldgm_1_ess(h_1_ess)) + x = torch.cat(tmp, dim=1) + return x + +class PershomReadout(nn.Module): + def __init__(self, + dataset, + num_struct_elements=None, + cls_hidden_dimension=None, + drop_out=None, + ): + + super().__init__() + assert isinstance(num_struct_elements, int) + #self.use_as_feature_extractor = True#False + + self.ldgm_0_up = SLayerRationalHat(num_struct_elements, 2, radius_init=0.1) + self.ldgm_0_down = SLayerRationalHat(num_struct_elements, 2, radius_init=0.1) + self.ldgm_cc= SLayerRationalHat(num_struct_elements, 2, radius_init=0.1) #SLayerExponential(num_struct_elements, 2)#SLayerSquare(num_struct_elements, 2) + self.ldgm_h1 = SLayerRationalHat(num_struct_elements, 2, radius_init=0.1)# SLayerExponential(num_struct_elements, 2)#SLayerSquare(num_struct_elements, 2) + + self.ldgm_0_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + self.ldgm_1_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + fc_in_feat = num_struct_elements#3*num_struct_elements + + self.cls_head = ClassifierHead( + dataset, + dim_in=fc_in_feat, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + def forward(self, h_0_up, h_0_down, h_0_cc, h_1): + tmp = [] + + tmp.append(self.ldgm_0_up(h_0_up)) + tmp.append(self.ldgm_0_down(h_0_down)) + tmp.append(self.ldgm_cc(h_0_cc)) + tmp.append(self.ldgm_h1(h_1)) + + x = torch.cat(tmp, dim=1) + return x + +class PershomClassifier(nn.Module): + def __init__(self, + dataset, + fc_in_feat =None, + cls_hidden_dimension=None, + drop_out=None, + ): + + super().__init__() + #assert isinstance(num_struct_elements, int) + self.use_as_feature_extractor = False + + self.cls_head = ClassifierHead( + dataset, + dim_in=fc_in_feat, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + def forward(self, x ): + if not self.use_as_feature_extractor: + x = self.cls_head(x) + + return x + + +class PershomBase(nn.Module): + def __init__(self, aug): + super().__init__() + self.readout_type = "extph" + self.supervised= True + self.use_super_level_set_filtration = None + self.use_as_feature_extractor = False + self.cls = None + self.augmentor= aug + + self.convs = torch.nn.ModuleList() + self.mlp= None + self.classifier_gnn= None + self.classifier_extpers= None + self.classifier_standardpers= None + self.repr2structelems= None + self.structelems2repr = None + self.standard_ph_readout = None + self.epochs= 1 + self.p= 0.01 + self.gnn_node = None + + def compute_extended_ph_link_tree(self,node_filt, batch, device): + ph_input= [] + for idx, (i, j, e) in enumerate(zip(batch.sample_pos[:-1], batch.sample_pos[1:], batch.boundary_info)): + v = node_filt[i:j]#extract vertex values\ + v.to("cpu") + e.to("cpu") + #use this for visualization + if idx==0 and len(batch.boundary_info)==1: + #print("v: ",v) + #print("edge_index: ", batch.edge_index) + #print("x: ", batch.x) + pass + ph_input.append((v, [e])) + pers = ph_extended_link_tree(ph_input) # ph_input needs to be: (v,[e]) + + h_0_up= [torch.stack([x.to(device) for x in g[0]]) for g in pers] + h_0_down= [torch.stack([x.to(device) for x in g[1]]) for g in pers] + h_0_extplus= [torch.stack([x.to(device) for x in g[2]]) for g in pers] + h_1_extminus= [torch.stack([x.to(device) for x in g[3]]) for g in pers] + + return h_0_up, h_0_down, h_0_extplus, h_1_extminus + + def forward(self, batch, device): + if batch.x is not None: + idx = torch.empty((batch.x.size(1),), dtype=torch.float32).uniform_(0, 1) < self.p + batch.x[:, idx] = 0 + node_filt0 = self.fil(batch) + + ##h= self.gnn(batch, device) + + if self.readout_type == "extph": + h_0_up_1, h_0_down_1, h_0_cc_1, h_1_1 = self.compute_extended_ph_link_tree(node_filt0, batch, device) + g1 = self.readout(h_0_up_1, h_0_down_1, h_0_cc_1, h_1_1) + elif self.readout_type == "sum": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_add_pool(node_filt0, batch.batch) + elif self.readout_type == "max": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_max_pool(node_filt0, batch.batch) + elif self.readout_type == "average": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_mean_pool(node_filt0, batch.batch) + elif self.readout_type == "sort": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_sort_pool(node_filt0, batch.batch, 400) + + out= g1 + out = self.classifier_gnn(out) + out= torch.nn.LogSoftmax(dim=1)(out) + return out + + @property + def feature_dimension(self): + return self.cls.cls_head[0].in_features + + @property + def use_as_feature_extractor(self): + return self.use_as_feature_extractor + + @use_as_feature_extractor.setter + def use_as_feature_extractor(self, val): + if hasattr(self, 'cls'): + self.cls.use_as_feature_extractor = val + + + def init_weights(self): + def init(m): + if isinstance(m, nn.Linear): + torch.nn.init.xavier_uniform_(m.weight) + #m.bias.data.fill_(0.01) + self.apply(init) + + +class ClassicReadoutFilt(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool = None, + use_node_degree: bool = None, + set_node_degree_uninformative: bool = None, + use_node_label: bool = None, + use_raw_node_label: bool = None, + filt_conv_number: int = None, + filt_conv_dimension: int = None, + gin_mlp_type: str = None, + num_struct_elements: int = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + conv_number: int = None, + conv_dimension: int = None, + augmentor=(None, None), + readout="sum", + **kwargs, + ): + super().__init__(augmentor) + self.readout_type = readout + + self.use_super_level_set_filtration = use_super_level_set_filtration + + self.fil = Filtration( + dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + filt_conv_number=filt_conv_number, + filt_conv_dimension=filt_conv_dimension, + gin_mlp_type=gin_mlp_type, + ) + + self.standard_ph_readout = StandardPershomReadout( + dataset, + num_struct_elements=num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + + self.cls = PershomClassifier(dataset, + fc_in_feat=num_struct_elements, # 3*num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.gnn = ClassicGNN(dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + gin_number=conv_number, # TODO: make this an argument + conv_type='GIN', # TODO: make this an argument + gin_dimension=conv_dimension, + gin_mlp_type=gin_mlp_type, + ) + self.supervised = True + if self.readout_type == "sort": + self.classifier_gnn = ClassifierHead(dataset, 400, cls_hidden_dimension, drop_out=drop_out) + else: + self.classifier_gnn = ClassifierHead(dataset, 1, cls_hidden_dimension, drop_out=drop_out) + self.init_weights() + + def reset_parameters(self): + self.fil.reset_parameters() + self.classifier_gnn.apply(weight_reset) + + +class PershomLearnedFilt(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool=None, + use_node_degree: bool=None, + set_node_degree_uninformative: bool=None, + use_node_label: bool=None, + use_raw_node_label: bool=None, + gin_number: int=None, + gin_dimension: int=None, + gin_mlp_type: str=None, + num_struct_elements: int=None, + cls_hidden_dimension: int=None, + drop_out: float=None, + augmentor= (None,None), + **kwargs, + ): + super().__init__(augmentor) + + + self.use_super_level_set_filtration = use_super_level_set_filtration + + self.fil = Filtration( + dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label= use_raw_node_label, + gin_number=gin_number, + gin_dimension=gin_dimension, + gin_mlp_type =gin_mlp_type, + ) + + self.readout = PershomReadout( + dataset, + num_struct_elements=num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.readout.use_as_feature_extractor= True + self.cls= PershomClassifier(dataset, + fc_in_feat= num_struct_elements,#3*num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.gnn= ClassicGNN(dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label= use_raw_node_label, + gin_number=gin_number, + gin_dimension=gin_dimension, + gin_mlp_type =gin_mlp_type, + ) + + self.init_weights() + self.supervised = False + +class PershomLearnedFiltSup(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool=None, + use_node_degree: bool=None, + set_node_degree_uninformative: bool=None, + use_node_label: bool=None, + use_raw_node_label: bool=None, + filt_conv_number: int=None, + filt_conv_dimension: int=None, + gin_mlp_type: str=None, + num_struct_elements: int=None, + cls_hidden_dimension: int=None, + drop_out: float=None, + conv_number: int= None, + conv_dimension: int= None, + augmentor= (None,None), + **kwargs, + ): + super().__init__(augmentor) + + self.use_super_level_set_filtration = use_super_level_set_filtration + + self.fil = Filtration( + dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label= use_raw_node_label, + filt_conv_number=filt_conv_number, + filt_conv_dimension=filt_conv_dimension, + gin_mlp_type =gin_mlp_type, + ) + + self.standard_ph_readout = StandardPershomReadout( + dataset, + num_struct_elements=num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + + self.readout = PershomReadout( + dataset, + num_struct_elements=num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.readout.use_as_feature_extractor= True + self.cls= PershomClassifier(dataset, + fc_in_feat= num_struct_elements,#3*num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.gnn= ClassicGNN(dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label= use_raw_node_label, + gin_number= conv_number, #TODO: make this an argument + conv_type= 'GIN', #TODO: make this an argument + gin_dimension=conv_dimension, + gin_mlp_type =gin_mlp_type, + ) + self.classifier_gnn= ClassifierHead(dataset, 4*num_struct_elements, cls_hidden_dimension, drop_out= drop_out) + self.classifier_extpers= ClassifierHead(dataset, 4*num_struct_elements, int(cls_hidden_dimension), drop_out= drop_out) + self.classifier_standardpers = ClassifierHead(dataset, 3 * num_struct_elements, int(cls_hidden_dimension), + drop_out=drop_out) + + self.supervised= True + self.repr2structelems = nn.Linear(conv_dimension,4*num_struct_elements) + self.structelems2repr = nn.Linear( 4 * num_struct_elements,conv_dimension) + self.init_weights() + + def reset_parameters(self): + self.fil.reset_parameters() + self.classifier_gnn.apply(weight_reset) + +class PershomRigidDegreeFilt(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool=None, + num_struct_elements: int=None, + cls_hidden_dimension: int=None, + drop_out: float=None, + **kwargs, + ): + super().__init__() + + + self.use_super_level_set_filtration = use_super_level_set_filtration + + self.fil = DegreeOnlyFiltration() + + self.cls = PershomClassifier( + dataset, + drop_out=drop_out, + cls_hidden_dimension=cls_hidden_dimension + ) + + self.init_weights() + self.supervised = False + +class OneHotEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + eye = torch.eye(dim, dtype=torch.float) + + self.register_buffer('eye', eye) + + def forward(self, batch): + assert batch.dtype == torch.long + + return self.eye.index_select(0, batch) + + @property + def dim(self): + return self.eye.size(1) + + +class UniformativeDummyEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + b = torch.ones(1, dim, dtype=torch.float) + self.register_buffer('ones', b) + + def forward(self, batch): + assert batch.dtype == torch.long + return self.ones.expand(batch.size(0), -1) + + @property + def dim(self): + return self.ones.size(1) + + +class GIN(nn.Module): + def __init__(self, + dataset, + use_node_degree: bool=None, + use_node_label: bool=None, + gin_number: int=None, + gin_dimension: int=None, + gin_mlp_type: str=None, + cls_hidden_dimension: int=None, + drop_out: float=None, + set_node_degree_uninformative: bool=None, + pooling_strategy: str=None, + **kwargs, + ): + super().__init__() + self.use_as_feature_extractor = False + self.pooling_strategy = pooling_strategy + self.gin_dimension = gin_dimension + + dim = gin_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if set_node_degree_uninformative and use_node_degree: + self.embed_deg = UniformativeDummyEmbedding(gin_dimension) + elif use_node_degree: + self.embed_deg = OneHotEmbedding(max_node_deg+1) + else: + self.embed_deg = None + + self.embed_lab = OneHotEmbedding(num_node_lab) if use_node_label else None + + dim_input = 0 + dim_input += self.embed_deg.dim if use_node_degree else 0 + dim_input += self.embed_lab.dim if use_node_label else 0 + assert dim_input > 0 + + dims = [dim_input] + (gin_number)*[dim] + self.convs = nn.ModuleList() + self.bns = nn.ModuleList() + self.act = torch.nn.functional.leaky_relu + + for n_1, n_2 in zip(dims[:-1], dims[1:]): + l = gin_mlp_factory(gin_mlp_type, n_1, n_2) + self.convs.append(GINConv(l, train_eps=True)) + self.bns.append(nn.BatchNorm1d(n_2)) + + if pooling_strategy == 'sum': + self.global_pool_fn = global_add_pool + elif pooling_strategy == 'sort': + self.k = int(np.percentile([d.num_nodes for d in dataset], 10)) + self.global_pool_fn = functools.partial(global_sort_pool, k=self.k) + self.sort_pool_nn = nn.Linear(self.k * gin_dimension, gin_dimension) + else: + raise ValueError + + self.cls = ClassifierHead( + dataset, + dim_in=gin_dimension, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + @property + def feature_dimension(self): + return self.cls.cls_head[0].in_features + + def forward(self, batch): + + node_deg = batch.node_deg + node_lab = batch.node_lab + + edge_index = batch.edge_index + + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + tmp = torch.cat(tmp, dim=1) + + z = [tmp] + + for conv, bn in zip(self.convs, self.bns): + x = conv(z[-1], edge_index) + x = bn(x) + x = self.act(x) + z.append(x) + + x = z[-1] + x = self.global_pool_fn(x, batch.batch) + + if self.pooling_strategy == 'sort': + x = self.sort_pool_nn(x) + x = x.squeeze() + + if not self.use_as_feature_extractor: + x = self.cls(x) + + return x + + +class SimpleNNBaseline(nn.Module): + def __init__(self, + dataset, + use_node_degree: bool=None, + use_node_label: bool=None, + set_node_degree_uninformative: bool=None, + gin_dimension: int=None, + gin_mlp_type: str=None, + cls_hidden_dimension: int=None, + drop_out: float=None, + pooling_strategy: str=None, + **kwargs, + ): + super().__init__() + self.use_as_feature_extractor = False + self.pooling_strategy = pooling_strategy + self.gin_dimension = gin_dimension + + dim = gin_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if set_node_degree_uninformative and use_node_degree: + self.embed_deg = UniformativeDummyEmbedding(gin_dimension) + elif use_node_degree: + self.embed_deg = OneHotEmbedding(max_node_deg+1) + else: + self.embed_deg = None + + self.embed_lab = OneHotEmbedding(num_node_lab) if use_node_label else None + + dim_input = 0 + dim_input += self.embed_deg.dim if use_node_degree else 0 + dim_input += self.embed_lab.dim if use_node_label else 0 + assert dim_input > 0 + + self.mlp = gin_mlp_factory(gin_mlp_type, dim_input, dim) + + if pooling_strategy == 'sum': + self.global_pool_fn = global_add_pool + elif pooling_strategy == 'sort': + self.k = int(np.percentile([d.num_nodes for d in dataset], 10)) + self.global_pool_fn = functools.partial(global_sort_pool, k=self.k) + self.sort_pool_nn = nn.Linear(self.k * gin_dimension, gin_dimension) + else: + raise ValueError + + self.cls = ClassifierHead( + dataset, + dim_in=gin_dimension, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + @property + def feature_dimension(self): + return self.cls.cls_head[0].in_features + + def forward(self, batch): + + node_deg = batch.node_deg + node_lab = batch.node_lab + + edge_index = batch.edge_index + + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + x = torch.cat(tmp, dim=1) + + x = self.mlp(x) + x = self.global_pool_fn(x, batch.batch) + + if self.pooling_strategy == 'sort': + x = self.sort_pool_nn(x) + x = x.squeeze() + + if not self.use_as_feature_extractor: + x = self.cls(x) + + return x + diff --git a/gnn/proteins_model.py b/gnn/proteins_model.py new file mode 100644 index 0000000..6c0c1e2 --- /dev/null +++ b/gnn/proteins_model.py @@ -0,0 +1,893 @@ +import torch +import numpy as np +import torch.nn as nn + +import functools + +from torchPHext.torchex_PHext.nn import SLayerRationalHat, SLayerSquare, SLayerExponential +from torch_geometric.nn import SAGEConv, LEConv, GINConv, global_add_pool, global_sort_pool, global_mean_pool, \ + global_max_pool +from torchPHext.torchex_PHext import pershom as pershom_ext +from torch_geometric.nn import GINConv, global_add_pool +import sys + +sys.path.append(".") + +ph_extended_link_tree = pershom_ext.pershom_backend.__C.VertExtendedFiltCompCuda_link_cut_tree__extended_persistence_batch + +def gin_mlp_factory(gin_mlp_type: str, dim_in: int, dim_out: int): + if gin_mlp_type == 'lin': + return nn.Linear(dim_in, dim_out) + + elif gin_mlp_type == 'lin_lrelu_lin': + return nn.Sequential( + nn.Linear(dim_in, dim_in), + nn.LeakyReLU(), + nn.Linear(dim_in, dim_out) + ) + + elif gin_mlp_type == 'lin_bn_lrelu_lin': + return nn.Sequential( + nn.Linear(dim_in, dim_in), + nn.BatchNorm1d(dim_in), + nn.LeakyReLU(), + nn.Linear(dim_in, dim_out) + ) + else: + raise ValueError("Unknown gin_mlp_type!") + + +def ClassifierHead( + dataset, + dim_in: int = None, + hidden_dim: int = None, + drop_out: float = None): + assert (0.0 <= drop_out) and (drop_out < 1.0) + assert dim_in is not None + assert drop_out is not None + assert hidden_dim is not None + + tmp = [ + nn.Linear(dim_in, hidden_dim), + nn.LeakyReLU(), + ] + + if drop_out > 0: + tmp += [nn.Dropout(p=drop_out)] + + tmp += [nn.Linear(hidden_dim, dataset.num_classes)] + + return nn.Sequential(*tmp) + + +class DegreeOnlyFiltration(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, batch): + tmp = [] + for i, j in zip(batch.sample_pos[:-1], batch.sample_pos[1:]): + max_deg = batch.node_deg[i:j].max() + + t = torch.ones(j - i, dtype=torch.float, device=batch.node_deg.device) + t = t * max_deg + tmp.append(t) + + max_deg = torch.cat(tmp, dim=0) + + normalized_node_deg = batch.node_deg.float() / max_deg + + return normalized_node_deg + + +class ClassicGNN(torch.nn.Module): + def __init__(self, + dataset, + use_node_degree=None, + set_node_degree_uninformative=None, + use_node_label=None, + use_raw_node_label=None, + gin_number=None, + gin_dimension=None, + conv_type='GIN', + gin_mlp_type=None, + cls_hidden_dimension=512, + drop_out=0.5, + **kwargs + ): + super().__init__() + + dim = gin_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if use_node_degree: + self.embed_deg = nn.Embedding(max_node_deg + 1, dim) + else: + self.embed_deg = None + + self.embed_lab = nn.Embedding(num_node_lab, dim) if use_node_label and num_node_lab is not None else None + self.use_raw_node_label = use_raw_node_label + + dim_input = dim*((self.embed_deg is not None) + (self.embed_lab is not None)) + + if (use_raw_node_label) and dataset[0].x is not None: + dim_input += dataset[0].x.size(1) + + dims = [dim_input] + (gin_number) * [dim] + + self.convs = nn.ModuleList() + self.bns = nn.ModuleList() + self.act = torch.nn.functional.leaky_relu + + if use_raw_node_label and dataset[0].x is not None: + self.node_label_embedder = torch.nn.Linear(dataset[0].x.size(1), gin_dimension) + + for n_1, n_2 in zip(dims[:-1], dims[1:]): + if conv_type == 'GIN': + l = gin_mlp_factory(gin_mlp_type, n_1, n_2) + self.convs.append(GINConv(l, train_eps=True)) + elif conv_type == 'GraphSAGE': + self.convs.append(SAGEConv(n_1, n_2)) + + self.bns.append(nn.BatchNorm1d(n_2)) + self.fc = None + self.classifier_extpers = ClassifierHead(dataset, gin_dimension, int(cls_hidden_dimension), + drop_out=drop_out) + + def forward2(self, h, batch): + h = global_add_pool(h, batch.batch) + out = self.classifier_extpers(h) + out = torch.nn.LogSoftmax(dim=1)(out) + return out + + def forward(self, batch, device): + + node_deg = batch.node_deg + if hasattr(batch, "node_lab"): + node_lab = batch.node_lab + else: + node_lab = None + edge_index = batch.edge_index + if self.use_raw_node_label: + if self.embed_deg is not None: + # tmp = [self.embed_deg(node_deg),self.node_label_embedder(node_lab.to(torch.float))] + tmp = [self.embed_deg(node_deg), batch.x] + else: + tmp = [batch.x] + else: + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + if len(tmp) > 0: + tmp = torch.cat(tmp, dim=1) + else: + tmp = torch.tensor(tmp) + x = tmp + for conv, bn in zip(self.convs[:-1], self.bns[:-1]): + x = conv(x, edge_index) + x = bn(x) + x = self.act(x) + x = torch.nn.Dropout(p=0.5)(x) + x = self.convs[-1](x, edge_index) + + return x + + +def weight_reset(m): + if isinstance(m, nn.Linear): + m.reset_parameters() + + +class Filtration(torch.nn.Module): + def __init__(self, + dataset, + use_node_degree=None, + set_node_degree_uninformative=None, + use_node_label=None, + use_raw_node_label=None, + filt_conv_number=None, + filt_conv_dimension=None, + gin_mlp_type=None, + **kwargs + ): + super().__init__() + + dim = filt_conv_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if use_node_degree: + self.embed_deg = nn.Embedding(max_node_deg + 1, dim) + else: + self.embed_deg = None + + self.embed_lab = nn.Embedding(num_node_lab, dim) if use_node_label and num_node_lab is not None else None + self.use_raw_node_label = use_raw_node_label + + dim_input = dim*((self.embed_deg is not None) + (self.embed_lab is not None)) + + if (use_raw_node_label) and dataset[0].x is not None: + dim_input += dataset[0].x.size(1) + + dims = [dim_input] + (filt_conv_number) * [dim] + + self.convs = nn.ModuleList() + self.bns = nn.ModuleList() + self.act = torch.nn.functional.leaky_relu + if use_raw_node_label and dataset[0].x is not None: + self.node_label_embedder = torch.nn.Linear(dataset[0].x.size(1), filt_conv_dimension) + + for n_1, n_2 in zip(dims[:-1], dims[1:]): + l = gin_mlp_factory(gin_mlp_type, n_1, n_2) + self.convs.append(GINConv(l, train_eps=True)) + self.bns.append(nn.BatchNorm1d(n_2)) + + self.fc = nn.Sequential( + nn.Linear(sum(dims), dim), + # nn.Linear(dims[-1], dim), + nn.BatchNorm1d(dim), + nn.LeakyReLU(), + nn.Linear(dim, 1), + nn.Sigmoid() + ) + + if dim_input==0: + raise Exception("Cannot have neither degree nor node features") + elif 0 in dims: + raise Exception("Cannot have 0 dim hidden layers") + + def reset_parameters(self): + for conv in self.convs: + conv.reset_parameters() + self.fc.apply(weight_reset) + + def forward(self, batch): + node_deg = batch.node_deg + if hasattr(batch, "node_lab"): + node_lab = batch.node_lab + else: + node_lab = None + edge_index = batch.edge_index + if self.use_raw_node_label: + if self.embed_deg is not None and self.fc is not None: + tmp = [self.embed_deg(node_deg), batch.x] # self.node_label_embedder(batch.x.to(torch.float))] + else: + tmp = [] + else: + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + tmp = torch.cat(tmp, dim=1) + + z = [tmp] + + for conv, bn in zip(self.convs, self.bns): + x = conv(z[-1], edge_index) + x = bn(x) + x = self.act(x) + z.append(x) + + x = torch.cat(z, dim=1) + ret = self.fc(x).squeeze() + return ret + + +class StandardPershomReadout(nn.Module): + def __init__(self, + dataset, + num_struct_elements=None, + cls_hidden_dimension=None, + drop_out=None, + ): + super().__init__() + assert isinstance(num_struct_elements, int) + # self.use_as_feature_extractor = True#False + + self.ldgm_0 = SLayerRationalHat(num_struct_elements, 2, radius_init=0.1) + self.ldgm_0_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + self.ldgm_1_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + fc_in_feat = num_struct_elements # 3*num_struct_elements + + self.cls_head = ClassifierHead( + dataset, + dim_in=fc_in_feat, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + def forward(self, h_0, h_0_ess, h_1_ess): + tmp = [] + + tmp.append(self.ldgm_0(h_0)) + tmp.append(self.ldgm_0_ess(h_0_ess)) + tmp.append(self.ldgm_1_ess(h_1_ess)) + x = torch.cat(tmp, dim=1) + return x + + +class PershomReadout(nn.Module): + def __init__(self, + dataset, + num_struct_elements=None, + cls_hidden_dimension=None, + drop_out=None, + ): + super().__init__() + assert isinstance(num_struct_elements, int) + # self.use_as_feature_extractor = True#False + + self.ldgm_0_up = SLayerRationalHat(num_struct_elements, 2, radius_init=0.1) + self.ldgm_0_down = SLayerRationalHat(num_struct_elements, 2, radius_init=0.1) + self.ldgm_cc = SLayerRationalHat(num_struct_elements, 2, + radius_init=0.1) # SLayerExponential(num_struct_elements, 2)#SLayerSquare(num_struct_elements, 2) + self.ldgm_h1 = SLayerRationalHat(num_struct_elements, 2, + radius_init=0.1) # SLayerExponential(num_struct_elements, 2)#SLayerSquare(num_struct_elements, 2) + + self.ldgm_0_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + self.ldgm_1_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + fc_in_feat = num_struct_elements # 3*num_struct_elements + + self.cls_head = ClassifierHead( + dataset, + dim_in=fc_in_feat, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + def forward(self, h_0_up, h_0_down, h_0_cc, h_1): + tmp = [] + + tmp.append(self.ldgm_0_up(h_0_up)) + tmp.append(self.ldgm_0_down(h_0_down)) + tmp.append(self.ldgm_cc(h_0_cc)) + tmp.append(self.ldgm_h1(h_1)) + + x = torch.cat(tmp, dim=1) + return x + + +class PershomClassifier(nn.Module): + def __init__(self, + dataset, + fc_in_feat=None, + cls_hidden_dimension=None, + drop_out=None, + ): + super().__init__() + # assert isinstance(num_struct_elements, int) + self.use_as_feature_extractor = False + self.cls_head = ClassifierHead( + dataset, + dim_in=fc_in_feat, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + def forward(self, x): + if not self.use_as_feature_extractor: + x = self.cls_head(x) + + return x + + +class PershomBase(nn.Module): + def __init__(self, aug): + super().__init__() + self.readout_type = "extph" + self.supervised = True + self.use_super_level_set_filtration = None + self.use_as_feature_extractor = False + self.cls = None + self.augmentor = aug + + self.convs = torch.nn.ModuleList() + self.mlp = None + self.classifier_gnn = None + self.classifier_extpers = None + self.classifier_standardpers = None + self.repr2structelems = None + self.structelems2repr = None + self.standard_ph_readout = None + self.epochs = 1 + self.p = 0.01 + self.gnn_node = None + + def compute_extended_ph_link_tree(self, node_filt, batch, device): + ph_input = [] + for idx, (i, j, e) in enumerate(zip(batch.sample_pos[:-1], batch.sample_pos[1:], batch.boundary_info)): + v = node_filt[i:j] # extract vertex values\ + v.to("cpu") + e.to("cpu") + # use this for visualization + if idx == 0 and len(batch.boundary_info) == 1: + # print("v: ",v) + # print("edge_index: ", batch.edge_index) + # print("x: ", batch.x) + pass + ph_input.append((v, [e])) + pers = ph_extended_link_tree(ph_input) # ph_input needs to be: (v,[e]) + + h_0_up = [torch.stack([x.to(device) for x in g[0]]) for g in pers] + h_0_down = [torch.stack([x.to(device) for x in g[1]]) for g in pers] + h_0_extplus = [torch.stack([x.to(device) for x in g[2]]) for g in pers] + h_1_extminus = [torch.stack([x.to(device) for x in g[3]]) for g in pers] + + return h_0_up, h_0_down, h_0_extplus, h_1_extminus + + def forward(self, batch, device): + if batch.x is not None: + idx = torch.empty((batch.x.size(1),), dtype=torch.float32).uniform_(0, 1) < self.p + batch.x[:, idx] = 0 + node_filt0 = self.fil(batch) + ##h= self.gnn(batch, device) + if self.readout_type == "extph": + h_0_up_1, h_0_down_1, h_0_cc_1, h_1_1 = self.compute_extended_ph_link_tree(node_filt0, batch, device) + g1 = self.readout(h_0_up_1, h_0_down_1, h_0_cc_1, h_1_1) + elif self.readout_type == "sum": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_add_pool(node_filt0, batch.batch) + elif self.readout_type == "max": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_max_pool(node_filt0, batch.batch) + elif self.readout_type == "average": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_mean_pool(node_filt0, batch.batch) + elif self.readout_type == "sort": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_sort_pool(node_filt0, batch.batch, 400) + out = g1 + out = self.classifier_gnn(out) + out = torch.nn.LogSoftmax(dim=1)(out) + return out + + @property + def feature_dimension(self): + return self.cls.cls_head[0].in_features + + @property + def use_as_feature_extractor(self): + return self.use_as_feature_extractor + + @use_as_feature_extractor.setter + def use_as_feature_extractor(self, val): + if hasattr(self, 'cls'): + self.cls.use_as_feature_extractor = val + + def init_weights(self): + def init(m): + if isinstance(m, nn.Linear): + torch.nn.init.xavier_uniform_(m.weight) + # m.bias.data.fill_(0.01) + + self.apply(init) + + +class ClassicReadoutFilt(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool = None, + use_node_degree: bool = None, + set_node_degree_uninformative: bool = None, + use_node_label: bool = None, + use_raw_node_label: bool = None, + filt_conv_number: int = None, + filt_conv_dimension: int = None, + gin_mlp_type: str = None, + num_struct_elements: int = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + conv_number: int = None, + conv_dimension: int = None, + augmentor=(None, None), + readout="sum", + **kwargs, + ): + super().__init__(augmentor) + self.readout_type = readout + + self.use_super_level_set_filtration = use_super_level_set_filtration + + self.fil = Filtration( + dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + filt_conv_number=filt_conv_number, + filt_conv_dimension=filt_conv_dimension, + gin_mlp_type=gin_mlp_type, + ) + + self.standard_ph_readout = StandardPershomReadout( + dataset, + num_struct_elements=num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + + self.cls = PershomClassifier(dataset, + fc_in_feat=num_struct_elements, # 3*num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.gnn = ClassicGNN(dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + gin_number=conv_number, + conv_type='GIN', + gin_dimension=conv_dimension, + gin_mlp_type=gin_mlp_type, + ) + self.supervised = True + if self.readout_type == "sort": + self.classifier_gnn = ClassifierHead(dataset, 400, cls_hidden_dimension, drop_out=drop_out) + else: + self.classifier_gnn = ClassifierHead(dataset, 1, cls_hidden_dimension, drop_out=drop_out) + self.init_weights() + + def reset_parameters(self): + self.fil.reset_parameters() + self.classifier_gnn.apply(weight_reset) + + +class PershomLearnedFilt(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool = None, + use_node_degree: bool = None, + set_node_degree_uninformative: bool = None, + use_node_label: bool = None, + use_raw_node_label: bool = None, + gin_number: int = None, + gin_dimension: int = None, + gin_mlp_type: str = None, + num_struct_elements: int = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + augmentor=(None, None), + readout="extph", + **kwargs, + ): + super().__init__(augmentor) + + self.readout_type = "extph" + self.use_super_level_set_filtration = use_super_level_set_filtration + + self.fil = Filtration( + dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + gin_number=gin_number, + gin_dimension=gin_dimension, + gin_mlp_type=gin_mlp_type, + ) + + self.readout = PershomReadout( + dataset, + num_struct_elements=num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.readout.use_as_feature_extractor = True + self.cls = PershomClassifier(dataset, + fc_in_feat=num_struct_elements, # 3*num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.gnn = ClassicGNN(dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + gin_number=gin_number, + gin_dimension=gin_dimension, + gin_mlp_type=gin_mlp_type, + ) + + self.init_weights() + self.supervised = False + + +class PershomLearnedFiltSup(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool = None, + use_node_degree: bool = None, + set_node_degree_uninformative: bool = None, + use_node_label: bool = None, + use_raw_node_label: bool = None, + filt_conv_number: int = None, + filt_conv_dimension: int = None, + gin_mlp_type: str = None, + num_struct_elements: int = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + conv_number: int = None, + conv_dimension: int = None, + augmentor=(None, None), + readout="extph", + **kwargs, + ): + super().__init__(augmentor) + + self.use_super_level_set_filtration = use_super_level_set_filtration + + self.fil = Filtration( + dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + filt_conv_number=filt_conv_number, + filt_conv_dimension=filt_conv_dimension, + gin_mlp_type=gin_mlp_type, + ) + + self.readout = PershomReadout( + dataset, + num_struct_elements=num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.readout.use_as_feature_extractor = True + self.classifier_gnn = ClassifierHead(dataset, 4 * num_struct_elements, cls_hidden_dimension, drop_out=drop_out) + self.supervised = True + self.init_weights() + + def reset_parameters(self): + self.fil.reset_parameters() + self.classifier_gnn.apply(weight_reset) + + +class PershomRigidDegreeFilt(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool = None, + num_struct_elements: int = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + **kwargs, + ): + super().__init__() + + self.use_super_level_set_filtration = use_super_level_set_filtration + + self.fil = DegreeOnlyFiltration() + + self.cls = PershomClassifier( + dataset, + drop_out=drop_out, + cls_hidden_dimension=cls_hidden_dimension + ) + + self.init_weights() + self.supervised = False + + +class OneHotEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + eye = torch.eye(dim, dtype=torch.float) + + self.register_buffer('eye', eye) + + def forward(self, batch): + assert batch.dtype == torch.long + + return self.eye.index_select(0, batch) + + @property + def dim(self): + return self.eye.size(1) + + +class UniformativeDummyEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + b = torch.ones(1, dim, dtype=torch.float) + self.register_buffer('ones', b) + + def forward(self, batch): + assert batch.dtype == torch.long + return self.ones.expand(batch.size(0), -1) + + @property + def dim(self): + return self.ones.size(1) + + +class GIN(nn.Module): + def __init__(self, + dataset, + use_node_degree: bool = None, + use_node_label: bool = None, + gin_number: int = None, + gin_dimension: int = None, + gin_mlp_type: str = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + set_node_degree_uninformative: bool = None, + pooling_strategy: str = None, + **kwargs, + ): + super().__init__() + self.use_as_feature_extractor = False + self.pooling_strategy = pooling_strategy + self.gin_dimension = gin_dimension + + dim = gin_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if set_node_degree_uninformative and use_node_degree: + self.embed_deg = UniformativeDummyEmbedding(gin_dimension) + elif use_node_degree: + self.embed_deg = OneHotEmbedding(max_node_deg + 1) + else: + self.embed_deg = None + + self.embed_lab = OneHotEmbedding(num_node_lab) if use_node_label and num_node_lab is not None else None + + dim_input = 0 + dim_input += self.embed_deg.dim if use_node_degree else 0 + dim_input += self.embed_lab.dim if use_node_label else 0 + assert dim_input > 0 + + dims = [dim_input] + (gin_number) * [dim] + self.convs = nn.ModuleList() + self.bns = nn.ModuleList() + self.act = torch.nn.functional.leaky_relu + + for n_1, n_2 in zip(dims[:-1], dims[1:]): + l = gin_mlp_factory(gin_mlp_type, n_1, n_2) + self.convs.append(GINConv(l, train_eps=True)) + self.bns.append(nn.BatchNorm1d(n_2)) + + if pooling_strategy == 'sum': + self.global_pool_fn = global_add_pool + elif pooling_strategy == 'sort': + self.k = int(np.percentile([d.num_nodes for d in dataset], 10)) + self.global_pool_fn = functools.partial(global_sort_pool, k=self.k) + self.sort_pool_nn = nn.Linear(self.k * gin_dimension, gin_dimension) + else: + raise ValueError + + self.cls = ClassifierHead( + dataset, + dim_in=gin_dimension, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + @property + def feature_dimension(self): + return self.cls.cls_head[0].in_features + + def forward(self, batch): + + node_deg = batch.node_deg + node_lab = batch.node_lab + + edge_index = batch.edge_index + + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + tmp = torch.cat(tmp, dim=1) + + z = [tmp] + + for conv, bn in zip(self.convs, self.bns): + x = conv(z[-1], edge_index) + x = bn(x) + x = self.act(x) + z.append(x) + + x = z[-1] + x = self.global_pool_fn(x, batch.batch) + + if self.pooling_strategy == 'sort': + x = self.sort_pool_nn(x) + x = x.squeeze() + + if not self.use_as_feature_extractor: + x = self.cls(x) + + return x + + +class SimpleNNBaseline(nn.Module): + def __init__(self, + dataset, + use_node_degree: bool = None, + use_node_label: bool = None, + set_node_degree_uninformative: bool = None, + gin_dimension: int = None, + gin_mlp_type: str = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + pooling_strategy: str = None, + **kwargs, + ): + super().__init__() + self.use_as_feature_extractor = False + self.pooling_strategy = pooling_strategy + self.gin_dimension = gin_dimension + + dim = gin_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if set_node_degree_uninformative and use_node_degree: + self.embed_deg = UniformativeDummyEmbedding(gin_dimension) + elif use_node_degree: + self.embed_deg = OneHotEmbedding(max_node_deg + 1) + else: + self.embed_deg = None + + self.embed_lab = OneHotEmbedding(num_node_lab) if use_node_label and num_node_lab is not None else None + + dim_input = 0 + dim_input += self.embed_deg.dim if use_node_degree else 0 + dim_input += self.embed_lab.dim if use_node_label else 0 + assert dim_input > 0 + + self.mlp = gin_mlp_factory(gin_mlp_type, dim_input, dim) + + if pooling_strategy == 'sum': + self.global_pool_fn = global_add_pool + elif pooling_strategy == 'sort': + self.k = int(np.percentile([d.num_nodes for d in dataset], 10)) + self.global_pool_fn = functools.partial(global_sort_pool, k=self.k) + self.sort_pool_nn = nn.Linear(self.k * gin_dimension, gin_dimension) + else: + raise ValueError + + self.cls = ClassifierHead( + dataset, + dim_in=gin_dimension, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + @property + def feature_dimension(self): + return self.cls.cls_head[0].in_features + + def forward(self, batch): + + node_deg = batch.node_deg + node_lab = batch.node_lab + + edge_index = batch.edge_index + + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + x = torch.cat(tmp, dim=1) + + x = self.mlp(x) + x = self.global_pool_fn(x, batch.batch) + + if self.pooling_strategy == 'sort': + x = self.sort_pool_nn(x) + x = x.squeeze() + + if not self.use_as_feature_extractor: + x = self.cls(x) + + return x + diff --git a/gnn/twocycles_model.py b/gnn/twocycles_model.py new file mode 100644 index 0000000..0843482 --- /dev/null +++ b/gnn/twocycles_model.py @@ -0,0 +1,922 @@ +import torch +import numpy as np +import torch.nn as nn + +import functools + +from torchPHext.torchex_PHext.nn import SLayerRationalHat, SLayerSquare, SLayerExponential +from torch_geometric.nn import SAGEConv, LEConv, GINConv, global_add_pool, global_sort_pool, global_max_pool, global_mean_pool +from torchPHext.torchex_PHext import pershom as pershom_ext +from torch_geometric.nn import GINConv, global_add_pool +import sys +sys.path.append(".") + +ph_extended_link_tree = pershom_ext.pershom_backend.__C.VertExtendedFiltCompCuda_link_cut_tree__extended_persistence_batch + +def gin_mlp_factory(gin_mlp_type: str, dim_in: int, dim_out: int): + if gin_mlp_type == 'lin': + return nn.Linear(dim_in, dim_out) + + elif gin_mlp_type == 'lin_lrelu_lin': + return nn.Sequential( + nn.Linear(dim_in, dim_in), + nn.LeakyReLU(), + nn.Linear(dim_in, dim_out) + ) + + elif gin_mlp_type == 'lin_bn_lrelu_lin': + return nn.Sequential( + nn.Linear(dim_in, dim_in), + nn.BatchNorm1d(dim_in), + nn.LeakyReLU(), + nn.Linear(dim_in, dim_out) + ) + else: + raise ValueError("Unknown gin_mlp_type!") + + +def ClassifierHead( + dataset, + dim_in: int=None, + hidden_dim: int=None, + drop_out: float=None): + assert (0.0 <= drop_out) and (drop_out < 1.0) + assert dim_in is not None + assert drop_out is not None + assert hidden_dim is not None + + tmp = [ + nn.Linear(dim_in, hidden_dim), + nn.LeakyReLU(), + ] + + if drop_out > 0: + tmp += [nn.Dropout(p=drop_out)] + + tmp += [nn.Linear(hidden_dim, dataset.num_classes)] + + return nn.Sequential(*tmp) + + +class DegreeOnlyFiltration(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, batch): + tmp = [] + for i, j in zip(batch.sample_pos[:-1], batch.sample_pos[1:]): + max_deg = batch.node_deg[i:j].max() + + t = torch.ones(j - i, dtype=torch.float, device=batch.node_deg.device) + t = t * max_deg + tmp.append(t) + + max_deg = torch.cat(tmp, dim=0) + + normalized_node_deg = batch.node_deg.float() / max_deg + + return normalized_node_deg + +class ClassicGNN(torch.nn.Module): + def __init__(self, + dataset, + use_node_degree=None, + set_node_degree_uninformative=None, + use_node_label=None, + use_raw_node_label= None, + gin_number=None, + gin_dimension=None, + conv_type= 'GIN', + gin_mlp_type = None, + cls_hidden_dimension= 512, + drop_out= 0.5, + **kwargs + ): + super().__init__() + + dim = gin_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if set_node_degree_uninformative and use_node_degree: + self.embed_deg = UniformativeDummyEmbedding(gin_dimension) + elif use_node_degree: + self.embed_deg = nn.Embedding(max_node_deg+1, dim) + else: + self.embed_deg = None + + self.embed_lab = nn.Embedding(num_node_lab, dim) if use_node_label else None + self.use_raw_node_label= use_raw_node_label + + dim_input = dim*((self.embed_deg is not None) + (self.embed_lab is not None)) + + if (use_raw_node_label) and dataset[0].x is not None: + dim_input+= dataset[0].x.size(1) + + dims = [dim_input] + (gin_number)*[dim] + + self.convs = nn.ModuleList() + self.bns = nn.ModuleList() + self.act = torch.nn.functional.leaky_relu + if use_raw_node_label: + self.node_label_embedder= torch.nn.Linear(num_node_lab, gin_dimension) + + for n_1, n_2 in zip(dims[:-1], dims[1:]): + if conv_type=='GIN': + l = gin_mlp_factory(gin_mlp_type, n_1, n_2) + self.convs.append(GINConv(l, train_eps=True)) + elif conv_type== 'GraphSAGE': + self.convs.append(SAGEConv(n_1, n_2)) + + self.bns.append(nn.BatchNorm1d(n_2)) + self.fc= None + self.classifier_extpers = ClassifierHead(dataset, gin_dimension, int(cls_hidden_dimension), + drop_out=drop_out) + def forward2(self, h, batch): + h= global_add_pool(h, batch.batch) + out= self.classifier_extpers(h) + out= torch.nn.LogSoftmax(dim=1)(out) + return out + def forward(self, batch, device): + + node_deg = batch.node_deg + if hasattr(batch, "node_lab"): + node_lab = batch.node_lab + else: + node_lab= None + edge_index = batch.edge_index + if self.use_raw_node_label: + if self.embed_deg is not None: + # tmp = [self.embed_deg(node_deg),self.node_label_embedder(node_lab.to(torch.float))] + tmp = [self.embed_deg(node_deg),batch.x] + else: + tmp= [] + raise ValueError + else: + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + if len(tmp)>0: + tmp = torch.cat(tmp, dim=1) + else: + tmp= torch.tensor(tmp) + x= tmp + for conv, bn in zip(self.convs[:-1], self.bns[:-1]): + x = conv(x, edge_index) + x = bn(x) + x = self.act(x) + x= torch.nn.Dropout(p=0.5)(x) + x= self.convs[-1](x,edge_index) + + return x +def weight_reset(m): + if isinstance(m, nn.Linear): + m.reset_parameters() +class Filtration(torch.nn.Module): + def __init__(self, + dataset, + use_node_degree=None, + set_node_degree_uninformative=None, + use_node_label=None, + use_raw_node_label= None, + filt_conv_number=None, + filt_conv_dimension=None, + gin_mlp_type = None, + **kwargs + ): + super().__init__() + + dim = filt_conv_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if use_node_degree: + self.embed_deg = nn.Embedding(max_node_deg+1, dim) + else: + self.embed_deg = None + + self.embed_lab = nn.Embedding(num_node_lab, dim) if (use_node_label and not use_raw_node_label and not ( + num_node_lab == 0 or num_node_lab is None)) else None + self.use_raw_node_label= use_raw_node_label + + dim_input = dim*((self.embed_deg is not None) + (self.embed_lab is not None)) + if (use_raw_node_label) and dataset[0].x is not None: + dim_input += dataset[0].x.shape(1) + + dims = [dim_input] + (filt_conv_number)*[dim] + + self.convs = nn.ModuleList() + self.bns = nn.ModuleList() + self.act = torch.nn.functional.leaky_relu + if use_raw_node_label: + self.node_label_embedder= torch.nn.Linear(dataset[0].x.size(1), filt_conv_dimension) + + for n_1, n_2 in zip(dims[:-1], dims[1:]): + l = gin_mlp_factory(gin_mlp_type, n_1, n_2) + self.convs.append(GINConv(l, train_eps=True)) + self.bns.append(nn.BatchNorm1d(n_2)) + + self.fc = nn.Sequential( + nn.Linear(sum(dims), dim), + nn.BatchNorm1d(dim), + nn.LeakyReLU(), + nn.Linear(dim, 1), + nn.Sigmoid() + ) + + if dim_input==0: + raise Exception("Cannot have neither degree nor node features") + elif 0 in dims: + raise Exception("Cannot have 0 dim hidden layers") + + def reset_parameters(self): + for conv in self.convs: + conv.reset_parameters() + self.fc.apply(weight_reset) + + def forward(self, batch): + + node_deg = batch.node_deg + if hasattr(batch, "node_lab"): + node_lab = batch.node_lab + else: + node_lab= None + edge_index = batch.edge_index + if self.use_raw_node_label: + if self.embed_deg is not None and self.fc is not None: + tmp = [self.embed_deg(node_deg), batch.x] + else: + tmp= [batch.x] + else: + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + tmp = torch.cat(tmp, dim=1) + + z = [tmp] + + for conv, bn in zip(self.convs, self.bns): + x = conv(z[-1], edge_index) + x = bn(x) + x = self.act(x) + z.append(x) + + x = torch.cat(z, dim=1) + ret = self.fc(x).squeeze() + return ret + +class StandardPershomReadout(nn.Module): + def __init__(self, + dataset, + num_struct_elements=None, + cls_hidden_dimension=None, + drop_out=None, + ): + + super().__init__() + assert isinstance(num_struct_elements, int) + #self.use_as_feature_extractor = True#False + + self.ldgm_0 = SLayerRationalHat(num_struct_elements, 2, radius_init=0.1) + self.ldgm_0_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + self.ldgm_1_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + fc_in_feat = num_struct_elements#3*num_struct_elements + + self.cls_head = ClassifierHead( + dataset, + dim_in=fc_in_feat, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + def forward(self, h_0,h_0_ess, h_1_ess): + tmp = [] + + tmp.append(self.ldgm_0(h_0)) + tmp.append(self.ldgm_0_ess(h_0_ess)) + tmp.append(self.ldgm_1_ess(h_1_ess)) + x = torch.cat(tmp, dim=1) + return x + +class PershomReadout(nn.Module): + def __init__(self, + dataset, + num_struct_elements=None, + cls_hidden_dimension=None, + drop_out=None, + ): + + super().__init__() + assert isinstance(num_struct_elements, int) + #self.use_as_feature_extractor = True#False + + self.ldgm_0_up = SLayerRationalHat(num_struct_elements, 2, radius_init=0.1) + self.ldgm_0_down = SLayerRationalHat(num_struct_elements, 2, radius_init=0.1) + self.ldgm_cc= SLayerRationalHat(num_struct_elements, 2, radius_init=0.1) #SLayerExponential(num_struct_elements, 2)#SLayerSquare(num_struct_elements, 2) + self.ldgm_h1 = SLayerRationalHat(num_struct_elements, 2, radius_init=0.1)# SLayerExponential(num_struct_elements, 2)#SLayerSquare(num_struct_elements, 2) + + self.ldgm_0_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + self.ldgm_1_ess = SLayerRationalHat(num_struct_elements, 1, radius_init=0.1) + fc_in_feat = num_struct_elements#3*num_struct_elements + + self.cls_head = ClassifierHead( + dataset, + dim_in=fc_in_feat, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + def forward(self, h_0_up, h_0_down, h_0_cc, h_1): + tmp = [] + + tmp.append(self.ldgm_0_up(h_0_up)) + tmp.append(self.ldgm_0_down(h_0_down)) + tmp.append(self.ldgm_cc(h_0_cc)) + tmp.append(self.ldgm_h1(h_1)) + + x = torch.cat(tmp, dim=1) + return x + +class PershomClassifier(nn.Module): + def __init__(self, + dataset, + fc_in_feat =None, + cls_hidden_dimension=None, + drop_out=None, + ): + + super().__init__() + #assert isinstance(num_struct_elements, int) + self.use_as_feature_extractor = False + + self.cls_head = ClassifierHead( + dataset, + dim_in=fc_in_feat, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + def forward(self, x ): + if not self.use_as_feature_extractor: + x = self.cls_head(x) + + return x + + +class PershomBase(nn.Module): + def __init__(self, aug): + super().__init__() + self.readout_type = "extph" + self.supervised= True + self.use_super_level_set_filtration = None + self.use_as_feature_extractor = False + self.cls = None + self.augmentor= aug + + self.convs = torch.nn.ModuleList() + self.mlp= None + self.classifier_gnn= None + self.classifier_extpers= None + self.classifier_standardpers= None + self.repr2structelems= None + self.structelems2repr = None + self.standard_ph_readout = None + self.epochs= 1 + self.p= 0.01 + self.gnn_node = None + + def compute_extended_ph_link_tree(self,node_filt, batch, device): + ph_input= [] + for idx, (i, j, e) in enumerate(zip(batch.sample_pos[:-1], batch.sample_pos[1:], batch.boundary_info)): + v = node_filt[i:j]#extract vertex values\ + v.to("cpu") + e.to("cpu") + #use this for visualization + if idx==0 and len(batch.boundary_info)==1: + #print("v: ",v) + #print("edge_index: ", batch.edge_index) + #print("x: ", batch.x) + pass + ph_input.append((v, [e])) + pers = ph_extended_link_tree(ph_input) # ph_input needs to be: (v,[e]) + + h_0_up= [torch.stack([x.to(device) for x in g[0]]) for g in pers] + h_0_down= [torch.stack([x.to(device) for x in g[1]]) for g in pers] + h_0_extplus= [torch.stack([x.to(device) for x in g[2]]) for g in pers] + h_1_extminus= [torch.stack([x.to(device) for x in g[3]]) for g in pers] + + return h_0_up, h_0_down, h_0_extplus, h_1_extminus + + def forward(self, batch, device): + + assert self.use_super_level_set_filtration is not None + if batch.x is not None: + idx = torch.empty((batch.x.size(1),), dtype=torch.float32).uniform_(0, 1) < self.p + batch.x[:, idx] = 0 + node_filt0 = self.fil(batch) + + ##h= self.gnn(batch, device) + + if self.readout_type == "extph": + h_0_up_1, h_0_down_1, h_0_cc_1, h_1_1 = self.compute_extended_ph_link_tree(node_filt0, batch, device) + g1 = self.readout(h_0_up_1, h_0_down_1, h_0_cc_1, h_1_1) + elif self.readout_type == "sum": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_add_pool(node_filt0, batch.batch) + elif self.readout_type == "max": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_max_pool(node_filt0, batch.batch) + elif self.readout_type == "average": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_mean_pool(node_filt0, batch.batch) + elif self.readout_type == "sort": + node_filt0 = node_filt0.unsqueeze(1) + g1 = global_sort_pool(node_filt0, batch.batch, 100) + + out= g1 + out = self.classifier_gnn(out) + out= torch.nn.LogSoftmax(dim=1)(out) + return out + + @property + def feature_dimension(self): + return self.cls.cls_head[0].in_features + + @property + def use_as_feature_extractor(self): + return self.use_as_feature_extractor + + @use_as_feature_extractor.setter + def use_as_feature_extractor(self, val): + if hasattr(self, 'cls'): + self.cls.use_as_feature_extractor = val + + + def init_weights(self): + def init(m): + if isinstance(m, nn.Linear): + torch.nn.init.xavier_uniform_(m.weight) + #m.bias.data.fill_(0.01) + self.apply(init) + + +class ClassicReadoutFilt(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool = None, + use_node_degree: bool = None, + set_node_degree_uninformative: bool = None, + use_node_label: bool = None, + use_raw_node_label: bool = None, + filt_conv_number: int = None, + filt_conv_dimension: int = None, + gin_mlp_type: str = None, + num_struct_elements: int = None, + cls_hidden_dimension: int = None, + drop_out: float = None, + conv_number: int = None, + conv_dimension: int = None, + augmentor=(None, None), + readout="sum", + **kwargs, + ): + super().__init__(augmentor) + self.readout_type = readout + + self.gnn_node = GNN_node_Virtualnode(5, 300) + self.use_super_level_set_filtration = use_super_level_set_filtration + + self.fil = Filtration( + dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + filt_conv_number=filt_conv_number, + filt_conv_dimension=filt_conv_dimension, + gin_mlp_type=gin_mlp_type, + ) + + self.standard_ph_readout = StandardPershomReadout( + dataset, + num_struct_elements=num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + + self.cls = PershomClassifier(dataset, + fc_in_feat=num_struct_elements, # 3*num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.gnn = ClassicGNN(dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label=use_raw_node_label, + gin_number=conv_number, # TODO: make this an argument + conv_type='GIN', # TODO: make this an argument + gin_dimension=conv_dimension, + gin_mlp_type=gin_mlp_type, + ) + self.supervised = True + if self.readout_type == "sort": + self.classifier_gnn = ClassifierHead(dataset, 100, cls_hidden_dimension, drop_out=drop_out) + else: + self.classifier_gnn = ClassifierHead(dataset, 1, cls_hidden_dimension, drop_out=drop_out) + self.init_weights() + + def reset_parameters(self): + self.fil.reset_parameters() + self.classifier_gnn.apply(weight_reset) + + +class PershomLearnedFilt(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool=None, + use_node_degree: bool=None, + set_node_degree_uninformative: bool=None, + use_node_label: bool=None, + use_raw_node_label: bool=None, + gin_number: int=None, + gin_dimension: int=None, + gin_mlp_type: str=None, + num_struct_elements: int=None, + cls_hidden_dimension: int=None, + drop_out: float=None, + augmentor= (None,None), + **kwargs, + ): + super().__init__(augmentor) + + + self.use_super_level_set_filtration = use_super_level_set_filtration + + self.fil = Filtration( + dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label= use_raw_node_label, + gin_number=gin_number, + gin_dimension=gin_dimension, + gin_mlp_type =gin_mlp_type, + ) + + self.readout = PershomReadout( + dataset, + num_struct_elements=num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.readout.use_as_feature_extractor= True + self.cls= PershomClassifier(dataset, + fc_in_feat= num_struct_elements,#3*num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.gnn= ClassicGNN(dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label= use_raw_node_label, + gin_number=gin_number, + gin_dimension=gin_dimension, + gin_mlp_type =gin_mlp_type, + ) + + self.init_weights() + self.supervised = False + +class PershomLearnedFiltSup(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool=None, + use_node_degree: bool=None, + set_node_degree_uninformative: bool=None, + use_node_label: bool=None, + use_raw_node_label: bool=None, + filt_conv_number: int=None, + filt_conv_dimension: int=None, + gin_mlp_type: str=None, + num_struct_elements: int=None, + cls_hidden_dimension: int=None, + drop_out: float=None, + conv_number: int= None, + conv_dimension: int= None, + augmentor= (None,None), + **kwargs, + ): + super().__init__(augmentor) + + self.gnn_node= GNN_node_Virtualnode(5, 300) + self.use_super_level_set_filtration = use_super_level_set_filtration + + self.fil = Filtration( + dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label= use_raw_node_label, + filt_conv_number=filt_conv_number, + filt_conv_dimension=filt_conv_dimension, + gin_mlp_type =gin_mlp_type, + ) + + self.standard_ph_readout = StandardPershomReadout( + dataset, + num_struct_elements=num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + + self.readout = PershomReadout( + dataset, + num_struct_elements=num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.readout.use_as_feature_extractor= True + self.cls= PershomClassifier(dataset, + fc_in_feat= num_struct_elements,#3*num_struct_elements, + cls_hidden_dimension=cls_hidden_dimension, + drop_out=drop_out + ) + self.gnn= ClassicGNN(dataset, + use_node_degree=use_node_degree, + set_node_degree_uninformative=set_node_degree_uninformative, + use_node_label=use_node_label, + use_raw_node_label= use_raw_node_label, + gin_number= conv_number, #TODO: make this an argument + conv_type= 'GIN', #TODO: make this an argument + gin_dimension=conv_dimension, + gin_mlp_type =gin_mlp_type, + ) + #self.mlp= nn.Linear(num_struct_elements*4,gin_dimension) + self.classifier_gnn= ClassifierHead(dataset, 4*num_struct_elements, cls_hidden_dimension, drop_out= drop_out) + self.classifier_extpers= ClassifierHead(dataset, 4*num_struct_elements, int(cls_hidden_dimension), drop_out= drop_out) + self.classifier_standardpers = ClassifierHead(dataset, 3 * num_struct_elements, int(cls_hidden_dimension), + drop_out=drop_out) + + self.supervised= True + self.repr2structelems = nn.Linear(conv_dimension,4*num_struct_elements) + self.structelems2repr = nn.Linear( 4 * num_struct_elements,conv_dimension) + self.init_weights() + + def reset_parameters(self): + self.fil.reset_parameters() + self.classifier_gnn.apply(weight_reset) + +class PershomRigidDegreeFilt(PershomBase): + def __init__(self, + dataset, + use_super_level_set_filtration: bool=None, + num_struct_elements: int=None, + cls_hidden_dimension: int=None, + drop_out: float=None, + **kwargs, + ): + super().__init__() + + + self.use_super_level_set_filtration = use_super_level_set_filtration + + self.fil = DegreeOnlyFiltration() + + self.cls = PershomClassifier( + dataset, + drop_out=drop_out, + cls_hidden_dimension=cls_hidden_dimension + ) + + self.init_weights() + self.supervised = False + +class OneHotEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + eye = torch.eye(dim, dtype=torch.float) + + self.register_buffer('eye', eye) + + def forward(self, batch): + assert batch.dtype == torch.long + + return self.eye.index_select(0, batch) + + @property + def dim(self): + return self.eye.size(1) + + +class UniformativeDummyEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + b = torch.ones(1, dim, dtype=torch.float) + self.register_buffer('ones', b) + + def forward(self, batch): + assert batch.dtype == torch.long + return self.ones.expand(batch.size(0), -1) + + @property + def dim(self): + return self.ones.size(1) + + +class GIN(nn.Module): + def __init__(self, + dataset, + use_node_degree: bool=None, + use_node_label: bool=None, + gin_number: int=None, + gin_dimension: int=None, + gin_mlp_type: str=None, + cls_hidden_dimension: int=None, + drop_out: float=None, + set_node_degree_uninformative: bool=None, + pooling_strategy: str=None, + **kwargs, + ): + super().__init__() + self.use_as_feature_extractor = False + self.pooling_strategy = pooling_strategy + self.gin_dimension = gin_dimension + + dim = gin_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if set_node_degree_uninformative and use_node_degree: + self.embed_deg = UniformativeDummyEmbedding(gin_dimension) + elif use_node_degree: + self.embed_deg = OneHotEmbedding(max_node_deg+1) + else: + self.embed_deg = None + + self.embed_lab = OneHotEmbedding(num_node_lab) if use_node_label else None + + dim_input = 0 + dim_input += self.embed_deg.dim if use_node_degree else 0 + dim_input += self.embed_lab.dim if use_node_label else 0 + assert dim_input > 0 + + dims = [dim_input] + (gin_number)*[dim] + self.convs = nn.ModuleList() + self.bns = nn.ModuleList() + self.act = torch.nn.functional.leaky_relu + + for n_1, n_2 in zip(dims[:-1], dims[1:]): + l = gin_mlp_factory(gin_mlp_type, n_1, n_2) + self.convs.append(GINConv(l, train_eps=True)) + self.bns.append(nn.BatchNorm1d(n_2)) + + if pooling_strategy == 'sum': + self.global_pool_fn = global_add_pool + elif pooling_strategy == 'sort': + self.k = int(np.percentile([d.num_nodes for d in dataset], 10)) + self.global_pool_fn = functools.partial(global_sort_pool, k=self.k) + self.sort_pool_nn = nn.Linear(self.k * gin_dimension, gin_dimension) + #nn.Conv1d( + # in_channels=gin_dimension, + # out_channels=gin_dimension, + # kernel_size=self.k + #) + else: + raise ValueError + + self.cls = ClassifierHead( + dataset, + dim_in=gin_dimension, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + @property + def feature_dimension(self): + return self.cls.cls_head[0].in_features + + def forward(self, batch): + + node_deg = batch.node_deg + node_lab = batch.node_lab + + edge_index = batch.edge_index + + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + tmp = torch.cat(tmp, dim=1) + + z = [tmp] + + for conv, bn in zip(self.convs, self.bns): + x = conv(z[-1], edge_index) + x = bn(x) + x = self.act(x) + z.append(x) + + x = z[-1] + x = self.global_pool_fn(x, batch.batch) + + if self.pooling_strategy == 'sort': + x = self.sort_pool_nn(x) + x = x.squeeze() + + if not self.use_as_feature_extractor: + x = self.cls(x) + + return x + + +class SimpleNNBaseline(nn.Module): + def __init__(self, + dataset, + use_node_degree: bool=None, + use_node_label: bool=None, + set_node_degree_uninformative: bool=None, + gin_dimension: int=None, + gin_mlp_type: str=None, + cls_hidden_dimension: int=None, + drop_out: float=None, + pooling_strategy: str=None, + **kwargs, + ): + super().__init__() + self.use_as_feature_extractor = False + self.pooling_strategy = pooling_strategy + self.gin_dimension = gin_dimension + + dim = gin_dimension + + max_node_deg = dataset.max_node_deg + num_node_lab = dataset.num_node_lab + + if set_node_degree_uninformative and use_node_degree: + self.embed_deg = UniformativeDummyEmbedding(gin_dimension) + elif use_node_degree: + self.embed_deg = OneHotEmbedding(max_node_deg+1) + else: + self.embed_deg = None + + self.embed_lab = OneHotEmbedding(num_node_lab) if use_node_label else None + + dim_input = 0 + dim_input += self.embed_deg.dim if use_node_degree else 0 + dim_input += self.embed_lab.dim if use_node_label else 0 + assert dim_input > 0 + + self.mlp = gin_mlp_factory(gin_mlp_type, dim_input, dim) + + if pooling_strategy == 'sum': + self.global_pool_fn = global_add_pool + elif pooling_strategy == 'sort': + self.k = int(np.percentile([d.num_nodes for d in dataset], 10)) + self.global_pool_fn = functools.partial(global_sort_pool, k=self.k) + self.sort_pool_nn = nn.Linear(self.k * gin_dimension, gin_dimension) + else: + raise ValueError + + self.cls = ClassifierHead( + dataset, + dim_in=gin_dimension, + hidden_dim=cls_hidden_dimension, + drop_out=drop_out + ) + + @property + def feature_dimension(self): + return self.cls.cls_head[0].in_features + + def forward(self, batch): + + node_deg = batch.node_deg + node_lab = batch.node_lab + + edge_index = batch.edge_index + + tmp = [e(x) for e, x in + zip([self.embed_deg, self.embed_lab], [node_deg, node_lab]) + if e is not None] + + x = torch.cat(tmp, dim=1) + + x = self.mlp(x) + x = self.global_pool_fn(x, batch.batch) + + if self.pooling_strategy == 'sort': + x = self.sort_pool_nn(x) + x = x.squeeze() + + if not self.use_as_feature_extractor: + x = self.cls(x) + + return x + diff --git a/imdb-multi_avg.sh b/imdb-multi_avg.sh new file mode 100644 index 0000000..32326f5 --- /dev/null +++ b/imdb-multi_avg.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for iter in {0..0} + do + dataset="imdb-multi" + name=${dataset}-${iter} + python3 -m train.imdb-multi_train_eval --filt_conv_number 2 --num_epochs 200 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} #&> imdb-multi-len-2-slurm-${SLURM_JOB_ID}.out + done diff --git a/imdb-multi_extpers.sh b/imdb-multi_extpers.sh new file mode 100644 index 0000000..32326f5 --- /dev/null +++ b/imdb-multi_extpers.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for iter in {0..0} + do + dataset="imdb-multi" + name=${dataset}-${iter} + python3 -m train.imdb-multi_train_eval --filt_conv_number 2 --num_epochs 200 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} #&> imdb-multi-len-2-slurm-${SLURM_JOB_ID}.out + done diff --git a/imdb-multi_max.sh b/imdb-multi_max.sh new file mode 100644 index 0000000..b944b73 --- /dev/null +++ b/imdb-multi_max.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for iter in {0..0} + do + dataset="imdb-multi" + name=${dataset}-${iter} + python3 -m train.imdb-multi_train_eval --readout max --filt_conv_number 2 --num_epochs 200 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} #&> imdb-multi-len-2-slurm-${SLURM_JOB_ID}.out + done diff --git a/imdb-multi_sum.sh b/imdb-multi_sum.sh new file mode 100644 index 0000000..ba48859 --- /dev/null +++ b/imdb-multi_sum.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for iter in {0..0} + do + dataset="imdb-multi" + name=${dataset}-${iter} + python3 -m train.imdb-multi_train_eval --readout average --filt_conv_number 2 --num_epochs 200 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} #&> imdb-multi-len-2-slurm-${SLURM_JOB_ID}.out + done diff --git a/molbace_avg.sh b/molbace_avg.sh new file mode 100755 index 0000000..96cad0e --- /dev/null +++ b/molbace_avg.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for iter in {0..0} + do + dataset="molbace" + name=${dataset}-${iter} + python3 -m train.mol_train_eval --readout average --dataset_name ogbg-molbace --lr 0.001 --filt_conv_number 2 --num_epochs 100 --batch_size 128 --use_raw_node_label True --output_dir ./results --device 0 --exp_name ${name} #&> dd-sum-len-2-slurm-${SLURM_JOB_ID}.out + done diff --git a/molbace_extpers.sh b/molbace_extpers.sh new file mode 100755 index 0000000..25b5aea --- /dev/null +++ b/molbace_extpers.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for iter in {0..0} + do + dataset="molbace" + name=${dataset}-${iter} + python3 -m train.mol_train_eval --dataset_name ogbg-molbace --lr 0.001 --readout extph --filt_conv_number 2 --num_epochs 100 --batch_size 128 --use_raw_node_label True --output_dir ./results --device 0 --exp_name ${name} #&> dd-sum-len-2-slurm-${SLURM_JOB_ID}.out + done diff --git a/molbace_max.sh b/molbace_max.sh new file mode 100755 index 0000000..2328650 --- /dev/null +++ b/molbace_max.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for iter in {0..0} + do + dataset="molbace" + name=${dataset}-${iter} + python3 -m train.mol_train_eval --readout max --dataset_name ogbg-molbace --lr 0.001 --filt_conv_number 2 --num_epochs 100 --batch_size 128 --use_raw_node_label True --output_dir ./results --device 0 --exp_name ${name} #&> dd-sum-len-2-slurm-${SLURM_JOB_ID}.out + done diff --git a/molbace_sum.sh b/molbace_sum.sh new file mode 100755 index 0000000..3a7e0c4 --- /dev/null +++ b/molbace_sum.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for iter in {0..0} + do + dataset="molbace" + name=${dataset}-${iter} + python3 -m train.mol_train_eval --readout sum --dataset_name ogbg-molbace --lr 0.001 --filt_conv_number 2 --num_epochs 100 --batch_size 128 --use_raw_node_label True --output_dir ./results --device 0 --exp_name ${name} #&> dd-sum-len-2-slurm-${SLURM_JOB_ID}.out + done diff --git a/molbbbp_avg.sh b/molbbbp_avg.sh new file mode 100755 index 0000000..3fb73c3 --- /dev/null +++ b/molbbbp_avg.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for iter in {0..0} + do + dataset="molbbbp" + name=${dataset}-${iter} + python3 -m train.mol_train_eval --readout average --dataset_name ogbg-molbbbp --lr 0.001 --filt_conv_number 2 --num_epochs 100 --batch_size 128 --use_raw_node_label True --output_dir ./results --device 0 --exp_name ${name} #&> dd-sum-len-2-slurm-${SLURM_JOB_ID}.out + done diff --git a/molbbbp_extpers.sh b/molbbbp_extpers.sh new file mode 100755 index 0000000..5ebfcb0 --- /dev/null +++ b/molbbbp_extpers.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for iter in {0..0} + do + dataset="molbbbp" + name=${dataset}-${iter} + python3 -m train.mol_train_eval --readout extph --dataset_name ogbg-molbbbp --lr 0.001 --readout extph --filt_conv_number 2 --num_epochs 100 --batch_size 128 --use_raw_node_label True --output_dir ./results --device 0 --exp_name ${name} #&> dd-sum-len-2-slurm-${SLURM_JOB_ID}.out + done diff --git a/molbbbp_max.sh b/molbbbp_max.sh new file mode 100755 index 0000000..8965d4a --- /dev/null +++ b/molbbbp_max.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for iter in {0..0} + do + dataset="molbbbp" + name=${dataset}-${iter} + python3 -m train.mol_train_eval --readout max --dataset_name ogbg-molbbbp --lr 0.001 --filt_conv_number 2 --num_epochs 100 --batch_size 128 --use_raw_node_label True --output_dir ./results --device 0 --exp_name ${name} #&> dd-sum-len-2-slurm-${SLURM_JOB_ID}.out + done diff --git a/molbbbp_sum.sh b/molbbbp_sum.sh new file mode 100755 index 0000000..008d47b --- /dev/null +++ b/molbbbp_sum.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for iter in {0..0} + do + dataset="molbbbp" + name=${dataset}-${iter} + python3 -m train.mol_train_eval --readout sum --dataset_name ogbg-molbbbp --lr 0.001 --filt_conv_number 2 --num_epochs 100 --batch_size 128 --use_raw_node_label True --output_dir ./results --device 0 --exp_name ${name} #&> dd-sum-len-2-slurm-${SLURM_JOB_ID}.out + done diff --git a/mutag_avg.sh b/mutag_avg.sh new file mode 100755 index 0000000..277d164 --- /dev/null +++ b/mutag_avg.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for iter in {0..0} + do + dataset="mutag" + name=${dataset}-${iter} + python3 -m train.mutag_train_eval --readout average --filt_conv_number 1 --num_epochs 100 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} #&> mutag-len-2-slurm-${SLURM_JOB_ID}.out + done diff --git a/mutag_extpers.sh b/mutag_extpers.sh new file mode 100755 index 0000000..829d4d1 --- /dev/null +++ b/mutag_extpers.sh @@ -0,0 +1,7 @@ +#!/bin/bash +for iter in {0..0} + do + dataset="mutag" + name=${dataset}-${iter} + python3 -m train.mutag_train_eval --filt_conv_number 1 --num_epochs 100 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} #&> mutag-len-2-iter${iter}.out + done diff --git a/mutag_max.sh b/mutag_max.sh new file mode 100644 index 0000000..413451a --- /dev/null +++ b/mutag_max.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for iter in {0..0} + do + dataset="mutag" + name=${dataset}-${iter} + python3 -m train.mutag_train_eval --readout max --filt_conv_number 1 --num_epochs 100 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} #&> mutag-len-2-slurm-${SLURM_JOB_ID}.out + done diff --git a/mutag_sum.sh b/mutag_sum.sh new file mode 100644 index 0000000..d36577f --- /dev/null +++ b/mutag_sum.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for iter in {0..0} + do + dataset="mutag" + name=${dataset}-${iter} + python3 -m train.mutag_train_eval --readout sum --filt_conv_number 1 --num_epochs 100 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} #&> mutag-len-2-slurm-${SLURM_JOB_ID}.out + done diff --git a/pinwheels_avg.sh b/pinwheels_avg.sh new file mode 100755 index 0000000..174229a --- /dev/null +++ b/pinwheels_avg.sh @@ -0,0 +1,7 @@ +#!/bin/bash +for iter in {0..0} + do + dataset="pinwheels" + name=${dataset}-${iter} + python3 -m train.pinwheels_train_eval --readout average --num_epochs 100 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} #&> imdb-multi-slurmfiles/slurm-${SLURM_JOB_ID}.out + done diff --git a/pinwheels_extpers.sh b/pinwheels_extpers.sh new file mode 100755 index 0000000..368cf2f --- /dev/null +++ b/pinwheels_extpers.sh @@ -0,0 +1,7 @@ +#!/bin/bash +for iter in {0..0} + do + dataset="pinwheels" + name=${dataset}-${iter} + python3 -m train.pinwheels_train_eval --num_epochs 100 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} #&> imdb-multi-slurmfiles/slurm-${SLURM_JOB_ID}.out + done diff --git a/pinwheels_max.sh b/pinwheels_max.sh new file mode 100755 index 0000000..5f7b41b --- /dev/null +++ b/pinwheels_max.sh @@ -0,0 +1,7 @@ +#!/bin/bash +for iter in {0..0} + do + dataset="pinwheels" + name=${dataset}-${iter} + python3 -m train.pinwheels_train_eval --readout max --num_epochs 100 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} #&> imdb-multi-slurmfiles/slurm-${SLURM_JOB_ID}.out + done diff --git a/pinwheels_sum.sh b/pinwheels_sum.sh new file mode 100755 index 0000000..6bb8cb5 --- /dev/null +++ b/pinwheels_sum.sh @@ -0,0 +1,7 @@ +#!/bin/bash +for iter in {0..0} + do + dataset="pinwheels" + name=${dataset}-${iter} + python3 -m train.pinwheels_train_eval --readout sum --num_epochs 100 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} #&> imdb-multi-slurmfiles/slurm-${SLURM_JOB_ID}.out + done diff --git a/proteins_extpers.sh b/proteins_extpers.sh new file mode 100755 index 0000000..0419f6d --- /dev/null +++ b/proteins_extpers.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for iter in {0..0} + do + dataset="proteins" + name=${dataset}${iter} + python3 -m train.proteins_train_eval --num_epochs 100 --batch_size 128 --use_raw_node_label True --output_dir ./results --device 0 --exp_name ${name} #&> imdb-multi-slurmfiles/slurm-${SLURM_JOB_ID}.out + done diff --git a/proteins_len_extpers.sh b/proteins_len_extpers.sh new file mode 100755 index 0000000..c3d5333 --- /dev/null +++ b/proteins_len_extpers.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for len in {2..5} + do + dataset="proteins" + name=${dataset}-${len} + python3 -m train.proteins_train_eval --filt_conv_number ${len} --num_epochs 50 --batch_size 128 --use_raw_node_label True --output_dir ./results --device 0 --exp_name ${name} &> proteins-len-${len}.out + done diff --git a/proteins_max.sh b/proteins_max.sh new file mode 100755 index 0000000..458d64f --- /dev/null +++ b/proteins_max.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for iter in {0..0} + do + dataset="proteins" + name=${dataset}-${iter} + python3 -m train.proteins_train_eval --readout max --filt_conv_number 2 --num_epochs 100 --batch_size 128 --use_raw_node_label True --output_dir ./results --device 0 --exp_name ${name} #&> proteins-max-len-2-slurm-${SLURM_JOB_ID}.out + done diff --git a/proteins_mean.sh b/proteins_mean.sh new file mode 100755 index 0000000..1300984 --- /dev/null +++ b/proteins_mean.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for iter in {0..0} + do + dataset="proteins" + name=${dataset}-${iter} + python3 -m train.proteins_train_eval --readout average --filt_conv_number 2 --num_epochs 100 --batch_size 128 --use_raw_node_label True --output_dir ./results --device 0 --exp_name ${name} #&> proteins-avg-len-2-slurm-${SLURM_JOB_ID}.out + done diff --git a/proteins_sum.sh b/proteins_sum.sh new file mode 100755 index 0000000..8b22116 --- /dev/null +++ b/proteins_sum.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for iter in {0..0} + do + dataset="proteins" + name=${name}-${iter} + python3 -m train.proteins_train_eval --readout sum --filt_conv_number 2 --num_epochs 100 --batch_size 128 --use_raw_node_label True --output_dir ./results --device 0 --exp_name ${name} #&> proteins-sum-len-2-slurm-${SLURM_JOB_ID}.out + done diff --git a/proteinslen1_sup.sh b/proteinslen1_sup.sh new file mode 100755 index 0000000..2cd60f9 --- /dev/null +++ b/proteinslen1_sup.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for len in {1..1} + do + dataset="proteins" + name=${dataset}-${len} + python3 -m train.proteins_train_eval --filt_conv_number ${len} --num_epochs 50 --batch_size 128 --use_raw_node_label True --output_dir ./results --device 0 --exp_name ${name} #&> len-${len}-slurm-${SLURM_JOB_ID}.out + done diff --git a/proteinslen2_sup.sh b/proteinslen2_sup.sh new file mode 100755 index 0000000..9ae6ba6 --- /dev/null +++ b/proteinslen2_sup.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for len in {2..2} + do + dataset="proteins" + name=${dataset}-${len} + python3 -m train.proteins_train_eval --filt_conv_number ${len} --num_epochs 50 --batch_size 128 --use_raw_node_label True --output_dir ./results --device 0 --exp_name ${name} #&> len-${len}-slurm-${SLURM_JOB_ID}.out + done diff --git a/proteinslen3_sup.sh b/proteinslen3_sup.sh new file mode 100755 index 0000000..8963af1 --- /dev/null +++ b/proteinslen3_sup.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for len in {3..3} + do + dataset="proteins" + name=${dataset}-${len} + python3 -m train.proteins_train_eval --filt_conv_number ${len} --num_epochs 50 --batch_size 128 --use_raw_node_label True --output_dir ./results --device 0 --exp_name ${name} &> len-${len}-slurm-${SLURM_JOB_ID}.out + done diff --git a/proteinslen4_sup.sh b/proteinslen4_sup.sh new file mode 100755 index 0000000..afa6cff --- /dev/null +++ b/proteinslen4_sup.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for len in {4..4} + do + dataset="proteins" + name=${dataset}-${len} + python3 -m train.proteins_train_eval --filt_conv_number ${len} --num_epochs 50 --batch_size 128 --use_raw_node_label True --output_dir ./results --device 0 --exp_name ${name} #&> len-${len}-slurm-${SLURM_JOB_ID}.out + done diff --git a/proteinslen5_sup.sh b/proteinslen5_sup.sh new file mode 100755 index 0000000..5a7f8a1 --- /dev/null +++ b/proteinslen5_sup.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +for len in {5..5} + do + name="proteins" + name=${name}-${len} + python3 -m train.proteins_train_eval --filt_conv_number ${len} --num_epochs 50 --batch_size 128 --use_raw_node_label True --output_dir ./results --device 0 --exp_name ${name} #&> len-${len}-slurm-${SLURM_JOB_ID}.out + done diff --git a/test_dd_all_args.sh b/test_dd_all_args.sh new file mode 100644 index 0000000..dc59595 --- /dev/null +++ b/test_dd_all_args.sh @@ -0,0 +1,19 @@ +#!/bin/bash + +name=dd +CUDA_VISIBLE_DEVICES=0 python3 -m train.dd_train_eval --filt_conv_number 2 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative False --use_raw_node_label False --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.dd_train_eval --filt_conv_number 2 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative False --use_raw_node_label False --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.dd_train_eval --filt_conv_number 2 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative True --use_raw_node_label False --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.dd_train_eval --filt_conv_number 2 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative False --use_raw_node_label True --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.dd_train_eval --filt_conv_number 2 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative False --use_raw_node_label False --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.dd_train_eval --filt_conv_number 2 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative True --use_raw_node_label False --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.dd_train_eval --filt_conv_number 2 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative True --use_raw_node_label True --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.dd_train_eval --filt_conv_number 2 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative False --use_raw_node_label True --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.dd_train_eval --filt_conv_number 2 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative False --use_raw_node_label True --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.dd_train_eval --filt_conv_number 2 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative False --use_raw_node_label False --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.dd_train_eval --filt_conv_number 2 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative True --use_raw_node_label False --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.dd_train_eval --filt_conv_number 2 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative True --use_raw_node_label True --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.dd_train_eval --filt_conv_number 2 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative True --use_raw_node_label True --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.dd_train_eval --filt_conv_number 2 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative False --use_raw_node_label True --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.dd_train_eval --filt_conv_number 2 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative True --use_raw_node_label False --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.dd_train_eval --filt_conv_number 2 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative True --use_raw_node_label True --use_node_label True diff --git a/test_imdb-multi_all_args.sh b/test_imdb-multi_all_args.sh new file mode 100644 index 0000000..ad05a93 --- /dev/null +++ b/test_imdb-multi_all_args.sh @@ -0,0 +1,19 @@ +#!/bin/bash + +name=imdb-multi +CUDA_VISIBLE_DEVICES=0 python3 -m train.imdb-multi_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative False --use_raw_node_label False --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.imdb-multi_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative False --use_raw_node_label False --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.imdb-multi_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative True --use_raw_node_label False --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.imdb-multi_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative False --use_raw_node_label True --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.imdb-multi_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative False --use_raw_node_label False --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.imdb-multi_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative True --use_raw_node_label False --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.imdb-multi_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative True --use_raw_node_label True --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.imdb-multi_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative False --use_raw_node_label True --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.imdb-multi_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative False --use_raw_node_label True --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.imdb-multi_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative False --use_raw_node_label False --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.imdb-multi_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative True --use_raw_node_label False --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.imdb-multi_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative True --use_raw_node_label True --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.imdb-multi_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative True --use_raw_node_label True --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.imdb-multi_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative False --use_raw_node_label True --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.imdb-multi_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative True --use_raw_node_label False --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.imdb-multi_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative True --use_raw_node_label True --use_node_label True diff --git a/test_mutag_all_args.sh b/test_mutag_all_args.sh new file mode 100644 index 0000000..827543e --- /dev/null +++ b/test_mutag_all_args.sh @@ -0,0 +1,19 @@ +#!/bin/bash + +name=mutag +CUDA_VISIBLE_DEVICES=0 python3 -m train.mutag_train_eval --readout sort --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative False --use_raw_node_label False --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.mutag_train_eval --readout sort --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative False --use_raw_node_label False --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.mutag_train_eval --readout sort --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative True --use_raw_node_label False --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.mutag_train_eval --readout sort --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative False --use_raw_node_label True --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.mutag_train_eval --readout sort --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative False --use_raw_node_label False --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.mutag_train_eval --readout sort --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative True --use_raw_node_label False --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.mutag_train_eval --readout sort --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative True --use_raw_node_label True --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.mutag_train_eval --readout sort --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative False --use_raw_node_label True --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.mutag_train_eval --readout sort --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative False --use_raw_node_label True --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.mutag_train_eval --readout sort --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative False --use_raw_node_label False --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.mutag_train_eval --readout sort --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative True --use_raw_node_label False --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.mutag_train_eval --readout sort --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative True --use_raw_node_label True --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.mutag_train_eval --readout sort --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative True --use_raw_node_label True --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.mutag_train_eval --readout sort --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative False --use_raw_node_label True --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.mutag_train_eval --readout sort --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative True --use_raw_node_label False --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.mutag_train_eval --readout sort --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative True --use_raw_node_label True --use_node_label True diff --git a/test_proteins_all_args.sh b/test_proteins_all_args.sh new file mode 100644 index 0000000..d86cdcb --- /dev/null +++ b/test_proteins_all_args.sh @@ -0,0 +1,19 @@ +#!/bin/bash + +name=proteins +CUDA_VISIBLE_DEVICES=0 python3 -m train.proteins_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative False --use_raw_node_label False --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.proteins_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative False --use_raw_node_label False --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.proteins_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative True --use_raw_node_label False --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.proteins_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative False --use_raw_node_label True --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.proteins_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative False --use_raw_node_label False --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.proteins_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative True --use_raw_node_label False --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.proteins_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative True --use_raw_node_label True --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.proteins_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative False --use_raw_node_label True --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.proteins_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative False --use_raw_node_label True --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.proteins_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative False --use_raw_node_label False --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.proteins_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative True --use_raw_node_label False --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.proteins_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative True --use_raw_node_label True --use_node_label False +CUDA_VISIBLE_DEVICES=0 python3 -m train.proteins_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree False --set_node_degree_uninformative True --use_raw_node_label True --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.proteins_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative False --use_raw_node_label True --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.proteins_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative True --use_raw_node_label False --use_node_label True +CUDA_VISIBLE_DEVICES=0 python3 -m train.proteins_train_eval --filt_conv_number 1 --num_epochs 2 --batch_size 128 --output_dir ./results --device 0 --exp_name ${name} --use_node_degree True --set_node_degree_uninformative True --use_raw_node_label True --use_node_label True diff --git a/torchPHext/.gitignore b/torchPHext/.gitignore new file mode 100644 index 0000000..7798908 --- /dev/null +++ b/torchPHext/.gitignore @@ -0,0 +1,9 @@ +develop.py +develop_2.py +.idea +.vscode +__pycache__ +*.pyc +.cache +.pytest_cache +pershom_dev/extensions_sandbox diff --git a/torchPHext/LICENSE b/torchPHext/LICENSE new file mode 100644 index 0000000..a6418fc --- /dev/null +++ b/torchPHext/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 Anonymous + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/torchPHext/README.md b/torchPHext/README.md new file mode 100644 index 0000000..20a1cfe --- /dev/null +++ b/torchPHext/README.md @@ -0,0 +1,7 @@ +# torchex_PHext + +This is a cpp/cuda extension to [PyTorch](http://pytorch.org) for Graph Contrastive Extended Filtration Learning (GCEFL). + +## Associated code: +main code is in torchPHext/pershom/pershom_cpp_src where extended_filt_persistence_batch() is run from vertex_filtration_comp_cuda.cpp. + diff --git a/torchPHext/torchex_PHext/__init__.py b/torchPHext/torchex_PHext/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/torchPHext/torchex_PHext/nn/__init__.py b/torchPHext/torchex_PHext/nn/__init__.py new file mode 100644 index 0000000..e154e8a --- /dev/null +++ b/torchPHext/torchex_PHext/nn/__init__.py @@ -0,0 +1,2 @@ +from .slayer import SLayerExponential, SLayerRational, SLayerRationalHat, SLayerSquare +from .modules import * diff --git a/torchPHext/torchex_PHext/nn/fuctional.py b/torchPHext/torchex_PHext/nn/fuctional.py new file mode 100644 index 0000000..e169be3 --- /dev/null +++ b/torchPHext/torchex_PHext/nn/fuctional.py @@ -0,0 +1,49 @@ +import torch + + +def histogram_intersection_loss(input: torch.Tensor, + target: torch.Tensor, + size_average: bool=True, + reduce: bool=True, + symetric_version: bool=True)->torch.Tensor: + r""" + This loss function is based on the `Histogram Intersection` score. + + The output is the *negative* Histogram Intersection Score. + + Args: + input (Tensor): :math:`(N, B)` where `N = batch size` and `B = number of classes` + target (Tensor): :math:`(N, B)` where `N = batch size` and `B = number of classes` + size_average (bool, optional): By default, the losses are averaged + over observations for each minibatch. However, if the field + :attr:`size_average` is set to ``False``, the losses are instead summed + for each minibatch. Ignored if :attr:`reduce` is ``False``. Default: ``True`` + reduce (bool, optional): + symetric_version (bool, optional): By default, the symetric version of histogram intersection + is used. If false the asymetric version is used. Default: ``True`` + + Returns: Tensor. + + """ + assert input.size() == target.size(), \ + "input.size() != target.size(): {} != {}!".format(input.size(), target.size()) + assert input.dim() == target.dim() == 2, \ + "input, target must be 2 dimensional. Got dim {} resp. {}".format(input.dim(), target.dim()) + + minima = input.min(target) + summed_minima = minima.sum(dim=1) + + if symetric_version: + normalization_factor = (input.sum(dim=1)).max(target.sum(dim=1)) + else: + normalization_factor = target.sum(dim=1) + + loss = summed_minima / normalization_factor + + if reduce: + loss = sum(loss) + + if size_average: + loss = loss / input.size(0) + + return -loss diff --git a/torchPHext/torchex_PHext/nn/modules.py b/torchPHext/torchex_PHext/nn/modules.py new file mode 100644 index 0000000..395741f --- /dev/null +++ b/torchPHext/torchex_PHext/nn/modules.py @@ -0,0 +1,45 @@ +import torch +import torch.nn as nn + + +class LinearView(nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x): + return x.view(x.size()[0], -1) + + +class Apply(nn.Module): + def __init__(self, function): + super().__init__() + self.function = function + + def forward(self, *args, **kwargs): + return self.function(*args, **kwargs) + + +class IndependentBranchesLinear(nn.Linear): + def __init__(self, in_features, out_features_branch, n_branches, bias=True): + assert in_features % n_branches == 0 + in_features_branch = int(in_features/n_branches) + super().__init__(in_features, out_features_branch*n_branches, bias) + + mask = torch.zeros_like(self.weight) + for i in range(n_branches): + mask[i*out_features_branch:(i+1)*out_features_branch, + i*in_features_branch:(i+1)*in_features_branch] = 1 + + self.register_buffer('mask', mask) + + def forward(self, inputs): + return torch.nn.functional.linear(inputs, self.weight * self.mask, self.bias) + + +class View(nn.Module): + def __init__(self, view_args): + super().__init__() + self.view_args = view_args + + def forward(self, input): + return input.view(*self.view_args) diff --git a/torchPHext/torchex_PHext/nn/slayer.py b/torchPHext/torchex_PHext/nn/slayer.py new file mode 100644 index 0000000..b099fc3 --- /dev/null +++ b/torchPHext/torchex_PHext/nn/slayer.py @@ -0,0 +1,562 @@ +r""" + +Implementation of **differentiable vectorization layers** for persistent +homology barcodes. + +For a basic tutorial click `here `_. +""" +import torch +import numpy as np +from torch import Tensor +from torch.nn.parameter import Parameter +from torch.nn.modules.module import Module + +from typing import List, Tuple + +import warnings + + +# region helper functions + + +def prepare_batch( + batch: List[Tensor], + point_dim: int=None + )->Tuple[Tensor, Tensor, int, int]: + """ + This method 'vectorizes' the multiset in order to take advances of GPU + processing. The policy is to embed all multisets in batch to the highest + dimensionality occurring in batch, i.e., ``max(t.size()[0]`` for ``t`` in batch). + + Args: + batch: + The input batch to process as a list of tensors. + + point_dim: + The dimension of the points the inputs consist of. + + Returns: + A four-tuple consisting of (1) the constructed ``batch``, i.e., a + tensor with size + ``batch_size`` x ``n_max_points`` x ``point_dim``; (2) a tensor + ``not_dummy`` of size ``batch_size`` x ``n_max_points``, where + ``1`` at position (i,j) indicates if the point is a dummy point, + whereas ``0`` indicates a dummy point used for padding; (3) + the max. number of points and (4) the batch size. + + Example:: + + >>> from chofer_torchex.nn.slayer import prepare_batch + >>> import torch + >>> x = [torch.rand(10,2), torch.rand(20,2)] + >>> batch, not_dummy, max_pts, batch_size = prepare_batch(x) + """ + if point_dim is None: + point_dim = batch[0].size(1) + assert (all(x.size(1) == point_dim for x in batch if len(x) != 0)) + + batch_size = len(batch) + batch_max_points = max([t.size(0) for t in batch]) + input_device = batch[0].device + + if batch_max_points == 0: + # if we are here, batch consists only of empty diagrams. + batch_max_points = 1 + + # This will later be used to set the dummy points to zero in the output. + not_dummy_points = torch.zeros( + batch_size, + batch_max_points, + device=input_device) + + prepared_batch = [] + + for i, multi_set in enumerate(batch): + n_points = multi_set.size(0) + + prepared_dgm = torch.zeros( + batch_max_points, + point_dim, + device=input_device) + + if n_points > 0: + index_selection = torch.tensor(range(n_points), + device=input_device) + + prepared_dgm.index_add_(0, index_selection, multi_set) + + not_dummy_points[i, :n_points] = 1 + + prepared_batch.append(prepared_dgm) + + prepared_batch = torch.stack(prepared_batch) + + return prepared_batch, not_dummy_points, batch_max_points, batch_size + + +def is_prepared_batch(input): + if not (isinstance(input, tuple) and len(input) == 4): + return False + else: + batch, not_dummy_points, max_points, batch_size = input + return isinstance(batch, Tensor) and isinstance(not_dummy_points, Tensor) and max_points > 0 and batch_size > 0 + + +def is_list_of_tensors(input): + try: + return all([isinstance(x, Tensor) for x in input]) + + except TypeError: + return False + + +def prepare_batch_if_necessary(input, point_dimension=None): + batch, not_dummy_points, max_points, batch_size = None, None, None, None + + if is_prepared_batch(input): + batch, not_dummy_points, max_points, batch_size = input + elif is_list_of_tensors(input): + if point_dimension is None: + point_dimension = input[0].size(1) + + batch, not_dummy_points, max_points, batch_size = prepare_batch( + input, + point_dimension) + + else: + raise ValueError( + 'SLayer does not recognize input format! Expecting [Tensor] or \ + prepared batch. Not {}'.format(input)) + + return batch, not_dummy_points, max_points, batch_size + + +def parameter_init_from_arg(arg, size, default, scalar_is_valid=False): + if isinstance(arg, (int, float)): + if not scalar_is_valid: + raise ValueError('Scalar initialization values are not valid. \ + Got {} expected Tensor of size {}.' + .format(arg, size)) + return torch.Tensor(*size).fill_(arg) + elif isinstance(arg, torch.Tensor): + assert(arg.size() == size) + return arg + elif arg is None: + if default in [torch.rand, torch.randn, torch.ones, torch.ones_like]: + return default(*size) + else: + return default(size) + else: + raise ValueError('Cannot handle parameter initialization. \ + Got "{}" '.format(arg)) + +# endregion + +class SLayerSquare(Module): + def __init__(self, n_elements: int, + point_dimension: int = 2, + multi_init: Tensor = None, + additive_init: Tensor = None): + """ + Args: + n_elements: + Number of structure elements used. + + point_dimension: D + Dimensionality of the points of which the + input multi set consists of. + + multi_init: + The initialization for the centers of the structure elements. + + additive_init: + Initialization for the sharpness of the structure elements. + """ + super().__init__() + + self.n_elements = n_elements + self.point_dimension = point_dimension + + expected_init_size = (self.n_elements, self.point_dimension) + + multi_init = parameter_init_from_arg( + multi_init, + expected_init_size, + torch.rand, scalar_is_valid=False) + additive_init = parameter_init_from_arg( + additive_init, + expected_init_size, + lambda size: torch.ones(*size) * 3) + + self.multi = Parameter(multi_init) + self.additive = Parameter(additive_init) + + def forward(self, input)->Tensor: + batch, not_dummy_points, max_points, batch_size = prepare_batch_if_necessary( + input, + point_dimension=self.point_dimension) + + batch = torch.cat([batch] * self.n_elements, 1) + + not_dummy_points = torch.cat([not_dummy_points] * self.n_elements, 1) + + + # centers = torch.cat([self.centers] * max_points, 1) + # centers = centers.view(-1, self.point_dimension) + # centers = torch.stack([centers] * batch_size, 0) + # + # sharpness = torch.pow(self.sharpness, 2) + # sharpness = torch.cat([sharpness] * max_points, 1) + # sharpness = sharpness.view(-1, self.point_dimension) + # sharpness = torch.stack([sharpness] * batch_size, 0) + + x = batch + x= torch.square(x[:,:,1]- x[:,:,0]) + #print("x.shape[1] for CC or H1", x) + + x= torch.nn.Linear(x.shape[1], self.n_elements, device= x.device)(x) + ##x= x*x.shape[1]*x.shape[1] + #x= x*not_dummy_points.shape[0]*not_dummy_points.shape[0] + # if x.shape[1]==self.n_elements: + # print("x if x.shape[1]==self.n_elements: ", x) + # else: + # print("x otherwise: ", x) + return x + +class SLayerExponential(Module): + """ + Proposed input layer for multisets [1]. + """ + def __init__(self, n_elements: int, + point_dimension: int=2, + centers_init: Tensor=None, + sharpness_init: Tensor=None): + """ + Args: + n_elements: + Number of structure elements used. + + point_dimension: D + Dimensionality of the points of which the + input multi set consists of. + + centers_init: + The initialization for the centers of the structure elements. + + sharpness_init: + Initialization for the sharpness of the structure elements. + """ + super().__init__() + + self.n_elements = n_elements + self.point_dimension = point_dimension + + expected_init_size = (self.n_elements, self.point_dimension) + + centers_init = parameter_init_from_arg( + centers_init, + expected_init_size, + torch.rand, scalar_is_valid=False) + sharpness_init = parameter_init_from_arg( + sharpness_init, + expected_init_size, + lambda size: torch.ones(*size)*3) + + self.centers = Parameter(centers_init) + self.sharpness = Parameter(sharpness_init) + + def forward(self, input)->Tensor: + batch, not_dummy_points, max_points, batch_size = prepare_batch_if_necessary( + input, + point_dimension=self.point_dimension) + + batch = torch.cat([batch] * self.n_elements, 1) + + not_dummy_points = torch.cat([not_dummy_points] * self.n_elements, 1) + + centers = torch.cat([self.centers] * max_points, 1) + centers = centers.view(-1, self.point_dimension) + centers = torch.stack([centers] * batch_size, 0) + + sharpness = torch.pow(self.sharpness, 2) + sharpness = torch.cat([sharpness] * max_points, 1) + sharpness = sharpness.view(-1, self.point_dimension) + sharpness = torch.stack([sharpness] * batch_size, 0) + + x = centers - batch + x = x.pow(2) + x = torch.mul(x, sharpness) + x = torch.sum(x, 2) + x = torch.exp(-x) + x = torch.mul(x, not_dummy_points) + x = x.view(batch_size, self.n_elements, -1) + x = torch.sum(x, 2) + #x = x.squeeze() + + return x + + def __repr__(self): + return 'SLayerExponential (... -> {} )'.format(self.n_elements) + + +class SLayerRational(Module): + """ + """ + def __init__(self, n_elements: int, + point_dimension: int=2, + centers_init: Tensor=None, + sharpness_init: Tensor=None, + exponent_init: Tensor=None, + pointwise_activation_threshold=None, + share_sharpness=False, + share_exponent=False, + freeze_exponent=True): + """ + Args: + n_elements: + Number of structure elements used. + + point_dimension: + Dimensionality of the points of which the input multi set + consists of. + + centers_init: + The initialization for the centers of the structure elements. + + sharpness_init: + Initialization for the sharpness of the structure elements. + """ + super().__init__() + + self.n_elements = int(n_elements) + self.point_dimension = int(point_dimension) + self.pointwise_activation_threshold = float(pointwise_activation_threshold) \ + if pointwise_activation_threshold is not None else None + self.share_sharpness = bool(share_sharpness) + self.share_exponent = bool(share_exponent) + self.freeze_exponent = freeze_exponent + + if self.pointwise_activation_threshold is not None: + self.pointwise_activation_threshold = float(self.pointwise_activation_threshold) + + centers_init = parameter_init_from_arg( + arg=centers_init, + size=(self.n_elements, self.point_dimension), + default=torch.rand) + + sharpness_init = parameter_init_from_arg( + arg=sharpness_init, + size=(1,) if self.share_sharpness else (self.n_elements, self.point_dimension), + default=torch.ones, + scalar_is_valid=True) + + exponent_init = parameter_init_from_arg( + arg=exponent_init, + size=(1,) if self.share_exponent else (self.n_elements,), + default=torch.ones, + scalar_is_valid=True) + + self.centers = Parameter(centers_init) + self.sharpness = Parameter(sharpness_init) + + if self.freeze_exponent: + self.register_buffer('exponent', exponent_init) + else: + self.exponent = Parameter(exponent_init) + + def forward(self, input)->Tensor: + batch, not_dummy_points, max_points, batch_size = prepare_batch_if_necessary( + input, + point_dimension=self.point_dimension) + + batch = batch.unsqueeze(1).expand( + batch_size, + self.n_elements, + max_points, + self.point_dimension) + + not_dummy_points = not_dummy_points.unsqueeze(1).expand(-1, self.n_elements, -1) + + centers = self.centers.unsqueeze(1).expand( + self.n_elements, + max_points, + self.point_dimension) + centers = centers.unsqueeze(0).expand(batch_size, *centers.size()) + + sharpness = self.sharpness + if self.share_sharpness: + sharpness = sharpness.expand(self.n_elements, self.point_dimension) + sharpness = sharpness.unsqueeze(1).expand(-1, max_points, -1) + sharpness = sharpness.unsqueeze(0).expand(batch_size, *sharpness.size()) + + exponent = self.exponent + if self.share_exponent: + exponent = exponent.expand(self.n_elements) + + exponent = exponent.unsqueeze(1).expand(-1, max_points) + exponent = exponent.unsqueeze(0).expand(batch_size, *exponent.size()) + + x = centers - batch + x = x.abs() + x = torch.mul(x, sharpness.abs()) + x = torch.sum(x, 3) + x = 1/(1+x).pow(exponent.abs()) + + if self.pointwise_activation_threshold is not None: + x[(x < self.pointwise_activation_threshold).data] = 0 + + x = torch.mul(x, not_dummy_points) + x = torch.sum(x, 2) + + return x + + def __repr__(self): + return 'SLayerRational (... -> {} )'.format(self.n_elements) + + +class SLayerRationalHat(Module): + """ + """ + def __init__(self, n_elements: int, + point_dimension: int=2, + centers_init: Tensor=None, + radius_init: float=1, + exponent: int=1 + ): + """ + Args: + n_elements: + Number of structure elements used. + + point_dimension: + Dimensionality of the points of which the input multi + set consists of. + + centers_init: + The initialization for the centers of the structure elements. + + radius_init: + Initialization for radius of zero level-set of the hat. + + exponent: + Exponent of the rationals forming the hat. + + """ + super().__init__() + + self.n_elements = int(n_elements) + self.point_dimension = int(point_dimension) + self.exponent = int(exponent) + + centers_init = parameter_init_from_arg(arg=centers_init, + size=(self.n_elements, self.point_dimension), + default=torch.rand) + + radius_init = parameter_init_from_arg(arg=radius_init, + size=(self.n_elements,), + default=torch.ones, + scalar_is_valid=True) + + self.centers = Parameter(centers_init) + self.radius = Parameter(radius_init) + self.norm_p = 1 + + def forward(self, input)->Tensor: + batch, not_dummy_points, max_points, batch_size = prepare_batch_if_necessary( + input, + point_dimension=self.point_dimension) + + batch = batch.unsqueeze(1).expand( + batch_size, + self.n_elements, + max_points, + self.point_dimension) + + not_dummy_points = not_dummy_points.unsqueeze(1).expand(-1, self.n_elements, -1) + + centers = self.centers.unsqueeze(1).expand( + self.n_elements, + max_points, + self.point_dimension) + centers = centers.unsqueeze(0).expand(batch_size, *centers.size()) + + radius = self.radius + radius = radius.unsqueeze(1).expand(-1, max_points) + radius = radius.unsqueeze(0).expand(batch_size, *radius.size()) + radius = radius.abs() + + norm_to_center = centers - batch + norm_to_center = torch.norm(norm_to_center, p=self.norm_p, dim=3) + + positive_part = 1/(1+norm_to_center).pow_(self.exponent) + + negative_part = 1/(1 + (radius - norm_to_center).abs_()).pow_(self.exponent) + + x = positive_part - negative_part + + x = torch.mul(x, not_dummy_points) + x = torch.sum(x, 2) + + return x + + def __repr__(self): + return 'SLayerRationalHat (... -> {} )'.format(self.n_elements) + + +class LinearRationalStretchedBirthLifeTimeCoordinateTransform: + def __init__(self, nu): + self._nu = nu + self._nu_squared = nu**2 + self._2_nu = 2*nu + + def __call__(self, dgm): + if len(dgm) == 0: + return dgm + + x, y = dgm[:, 0], dgm[:, 1] + y = y - x + + i = (y <= self._nu) + y[i] = - self._nu_squared/y[i] + self._2_nu + + return torch.stack([x, y], dim=1) + + +class LogStretchedBirthLifeTimeCoordinateTransform: + def __init__(self, nu): + self.nu = nu + + def __call__(self, dgm): + if len(dgm) == 0: + return dgm + + x, y = dgm[:, 0], dgm[:, 1] + y = y - x + + i = (y <= self.nu) + y[i] = torch.log(y[i] / self.nu)*self.nu + self.nu + + return torch.stack([x, y], dim=1) + + +class UpperDiagonalThresholdedLogTransform: + def __init__(self, nu): + self.b_1 = (torch.Tensor([1, 1]) / np.sqrt(2)) + self.b_2 = (torch.Tensor([-1, 1]) / np.sqrt(2)) + self.nu = nu + + def __call__(self, dgm): + if len(dgm) == 0: + return dgm + + self.b_1 = self.b_1.to(dgm.device) + self.b_2 = self.b_2.to(dgm.device) + + x = torch.mul(dgm, self.b_1.repeat(dgm.size(0), 1)) + x = torch.sum(x, 1).squeeze() + y = torch.mul(dgm, self.b_2.repeat(dgm.size(0), 1)) + y = torch.sum(y, 1).squeeze() + i = (y <= self.nu) + y[i] = torch.log(y[i] / self.nu)*self.nu + self.nu + ret = torch.stack([x, y], 1) + return ret diff --git a/torchPHext/torchex_PHext/pershom/__init__.py b/torchPHext/torchex_PHext/pershom/__init__.py new file mode 100644 index 0000000..0cb7d2c --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/__init__.py @@ -0,0 +1 @@ +from .pershom_backend import * diff --git a/torchPHext/torchex_PHext/pershom/pershom_backend.py b/torchPHext/torchex_PHext/pershom/pershom_backend.py new file mode 100644 index 0000000..340a1e5 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_backend.py @@ -0,0 +1,270 @@ +r""" +This module exposes the C++/CUDA backend functionality for Python. + +Terminology +----------- + +Descending sorted boundary array: + Boundary array which encodes the boundary matrix (BM) for a given + filtration in column first order. + Let BA be the descending_sorted_boundary of BM, then + ``BA[i, :]`` is the i-th column of BM. + Content encoded as decreasingly sorted list, embedded into the array + with -1 padding from the right. + + Example : + ``BA[3, :] = [2, 0, -1, -1]`` + then :math:`\partial(v_3) = v_0 + v_2` + + ``BA[6, :] = [5, 4, 3, -1]`` + then :math:`\partial(v_6) = v_3 + v_4 + v_5` + + +Compressed descending sorted boundary array: + Same as *descending sorted boundary array* but rows consisting only of -1 + are omitted. + This is sometimes used for efficiency purposes and is usually accompanied + by a vector, ``v``, telling which row of the reduced BA corresponds to + which row of the uncompressed BA, i.e., ``v[3] = 5`` means that the 3rd + row of the reduced BA corresponds to the 5th row in the uncompressed + version. + +Birth/Death-time: + Index of the coresponding birth/death event in the filtration. + This is always an *integer*. + +Birth/Death-value: + If a filtration is induced by a real-valued function, this corresponds + to the value of this function corresponding to the birth/death event. + This is always *real*-valued. + +Limitations +----------- + +Currently all ``cuda`` backend functionality **only** supports ``int64_t`` and +``float32_t`` typing. + +""" +import os.path as pth +from typing import List +from torch import Tensor +from glob import glob + + +from torch.utils.cpp_extension import load + + +__module_file_dir = pth.dirname(pth.realpath(__file__)) +__cpp_src_dir = pth.join(__module_file_dir, 'pershom_cpp_src') +src_files = [] + +for extension in ['*.cpp']:#, '*.cu']: + src_files += glob(pth.join(__cpp_src_dir, extension)) + +#TODO: -I pershom_cpp_src + +# jit compiling the c++ extension + +#try: +__C = load( + 'pershom_cuda_ext', + src_files, + extra_include_paths= [__cpp_src_dir, pth.join(__cpp_src_dir, "phat/include/")], + with_cuda= False, + verbose=True)#False + +#except Exception as ex: +# print("Error in {}. Failed jit compilation. Maybe your CUDA environment \ +# is messed up?".format(__file__)) +# print("Error was {}".format(ex)) + + +def find_merge_pairings( + pivots: Tensor, + max_pairs: int = -1 + )->Tensor: + """Finds the pairs which have to be merged in the current iteration of the + matrix reduction. + + Args: + pivots: + The pivots of a descending sorted boundary array. + Expected size is ``Nx1``, where N is the number of columns of the + underlying descending sorted boundary array. + + max_pairs: + The output is at most a ``max_pairs x 2`` Tensor. If set to + default all possible merge pairs are returned. + + Returns: + The merge pairs, ``p``, for the current iteration of the reduction. + ``p[i]`` is a merge pair. + In boundary matrix notation this would mean column ``p[i][0]`` has to + be merged into column ``p[i][1]``. + """ + return __C.CalcPersCuda__find_merge_pairings(pivots, max_pairs) + + +def merge_columns_( + compr_desc_sort_ba: Tensor, + merge_pairs: Tensor + )->None: + r"""Executes the given merging operations inplace on the descending + sorted boundary array. + + Args: + compr_desc_sort_ba: + see module description top. + + merge_pairs: + output of a ``find_merge_pairings`` call. + + Returns: + None + """ + __C.CalcPersCuda__merge_columns_(compr_desc_sort_ba, merge_pairs) + + +def read_barcodes( + pivots: Tensor, + simplex_dimension: Tensor, + max_dim_to_read_of_reduced_ba: int + )->List[List[Tensor]]: + """Reads the barcodes using the pivot of a reduced boundary array. + + Arguments: + pivots: + pivots is the first column of a compr_desc_sort_ba + + simplex_dimension: + Vector whose i-th entry is the dimension if the i-th simplex in + the given filtration. + + max_dim_to_read_of_reduced_ba: + features up to max_dim_to_read_of_reduced_ba are read from the + reduced boundary array + + Returns: + List of birth/death times. + + ``ret[0][n]`` are non essential birth/death-times of dimension ``n``. + + ``ret[1][n]`` are birth-times of essential classes of dimension ``n``. + """ + return __C.CalcPersCuda__read_barcodes( + pivots, + simplex_dimension, + max_dim_to_read_of_reduced_ba) + + +def calculate_persistence( + compr_desc_sort_ba: Tensor, + ba_row_i_to_bm_col_i: Tensor, + simplex_dimension: Tensor, + max_dim_to_read_of_reduced_ba: int, + max_pairs: int = -1 + )->List[List[Tensor]]: + """Returns the barcodes of the given encoded boundary array. + + Arguments: + compr_desc_sort_ba: + A `compressed descending sorted boundary array`, + see readme section top. + + ba_row_i_to_bm_col_i: + Vector whose i-th entry is the column index of the boundary + matrix the i-th row in ``compr_desc_sort_ba corresponds`` to. + + simplex_dimension: + Vector whose i-th entry is the dimension if the i-th simplex in + the given filtration + + max_pairs: see ``find_merge_pairings``. + + max_dim_to_read_of_reduced_ba: + features up to max_dim_to_read_of_reduced_ba are read from the + reduced boundary array. + + Returns: + List of birth/death times. + + ``ret[0][n]`` are non essential birth/death-times of dimension ``n``. + + ``ret[1][n]`` are birth-times of essential classes of dimension ``n``. + """ + return __C.CalcPersCuda__calculate_persistence( + compr_desc_sort_ba, + ba_row_i_to_bm_col_i, + simplex_dimension, + max_dim_to_read_of_reduced_ba, + max_pairs) + + +def vr_persistence_l1( + point_cloud: Tensor, + max_dimension: int, + max_ball_diameter: float=0.0 + )->List[List[Tensor]]: + """Returns the barcodes of the Vietoris-Rips complex of a given point cloud + w.r.t. the l1 (manhatten) distance. + + Args: + point_cloud: + Point cloud from which the Vietoris-Rips complex is generated. + + max_dimension: + The dimension of the used Vietoris-Rips complex. + + max_ball_diameter: + If not 0, edges whose two defining vertices' distance is greater + than ``max_ball_diameter`` are ignored. + + Returns: + List of birth/death times. + + ``ret[0][n]`` are non essential birth/death-*values* of dimension ``n``. + + ``ret[1][n]`` are birth-*values* of essential classes of + dimension ``n``. + """ + return __C.VRCompCuda__vr_persistence_l1( + point_cloud, + max_dimension, + max_ball_diameter) + + +def vr_persistence( + distance_matrix: Tensor, + max_dimension: int, + max_ball_diameter: float=0.0 + )->List[List[Tensor]]: + """Returns the barcodes of the Vietoris-Rips complex of a given distance + matrix. + + **Note**: ``distance_matrix`` is assumed to be a square matrix. + Practically, symmetry is *not* required and the upper diagonal part is + *ignored*. For the computation, just the *lower* diagonal part is used. + + Args: + distance_matrix: + Distance matrix the Vietoris-Rips complex is based on. + + max_dimension: + The dimension of the used Vietoris-Rips complex. + + max_ball_diameter: + If not 0, edges whose two defining vertices' distance is greater + than ``max_ball_diameter`` are ignored. + + Returns: + List of birth/death times. + + ``ret[0][n]`` are non essential birth/death-*values* of dimension ``n``. + + ``ret[1][n]`` are birth-*values* of essential classes of + dimension ``n``. + """ + return __C.VRCompCuda__vr_persistence( + distance_matrix, + max_dimension, + max_ball_diameter) diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/calc_pers_cuda.cu b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/calc_pers_cuda.cu new file mode 100644 index 0000000..f81dfd0 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/calc_pers_cuda.cu @@ -0,0 +1,621 @@ +#include +#include + +#include +#include + +#include +#include + +#include "tensor_utils.cuh" +#include "param_checks_cuda.cuh" +#include "cuda_checks.cuh" + +//HINT: lookup the torch library at ~/.local/lib/python3.8/site-packages/torch +using namespace torch; + + +#pragma region find_merge_pairings + +namespace CalcPersCuda +{ + +namespace +{ + +template +__global__ void find_left_slicings_indices_cuda_kernel( + scalar_t *__restrict__ input, + scalar_t *__restrict__ output, + size_t input_size) +{ + + const int index_middle = blockIdx.x * blockDim.x + threadIdx.x; + const int index_left = index_middle - 1; + const int index_right = index_middle + 1; + + if (index_right < input_size) + { + + const int value_left = (index_left != -1) ? input[index_left] : -1; //OPTIMIZE: if we could + // pad input with -1 on the left this conditional would be obsolete + const int value_middle = input[index_middle]; + const int value_right = input[index_right]; + if (value_left != value_middle && + value_middle == value_right) + { + output[index_middle] = index_middle; + } + } +} + +template +__global__ void find_right_slicings_indices_cuda_kernel( + scalar_t *__restrict__ input, + scalar_t *__restrict__ output, + size_t input_size) +{ + + const int index_left = blockIdx.x * blockDim.x + threadIdx.x; + const int index_middle = index_left + 1; + const int index_right = index_middle + 1; + + if (index_middle < input_size) + { + + const int value_left = input[index_left]; + const int value_middle = input[index_middle]; + const int value_right = (index_right < input_size) ? input[index_right] : (value_middle + 1); //OPTIMIZE: if we could + // pad input with inf on the right this conditional would be obsolete + if (value_left == value_middle && + value_middle != value_right) + { + output[index_middle] = index_middle + 1; + } + } +} + +} // namespace + +/** + * @brief Finds the indices for slicing the sorted pivots values. + * Example: + * pivots.sort(0)[0] = [-1, -1, 2, 2, 2, 4, 4] -> [[2, 4], [5, 6]] + * + * @tparam scalar_t + * @param pivots + * @return Tensor return.dtype() == scalar_t + */ +template +Tensor find_slicing_indices_cuda_kernel_call( + const Tensor & pivots) +{ + at::OptionalDeviceGuard guard(device_of(pivots)); + + Tensor output = torch::zeros_like(pivots).fill_(-1); + const int threads_per_block = 256; + const int blocks = pivots.size(0) / threads_per_block + 1; + + find_left_slicings_indices_cuda_kernel<<>>( + pivots.data(), + output.data(), + pivots.size(0)); + + cudaCheckError(); + + find_right_slicings_indices_cuda_kernel<<>>( + pivots.data(), + output.data(), + pivots.size(0)); + + cudaCheckError(); + + output = output.masked_select(output.ge(0)); + output = output.view(IntList({output.size(0) / 2, 2})); + + return output; +} + +namespace +{ + +/** + * @brief Implements a batch version of traditional slice for + * a input vector and a tensor which defines the slicings. + * The output is a then of dimension + * slicings.size(0) x (slicings[:, 1] - slicings[:, 0]).max() + * + * @param p_input + * @param p_slicings + * @param p_return_value + * @param return_value_size_0 + * @param return_value_size_1 + */ +__global__ void extract_slicings_cuda_kernel( + int64_t *p_input, + int64_t *p_slicings, + int64_t *p_return_value, + int64_t return_value_size_0, + int64_t return_value_size_1) +{ + + const int thread_id = blockIdx.x * blockDim.x + threadIdx.x; + + if (thread_id < return_value_size_0) + { + auto p_return_value_row = p_return_value + thread_id * return_value_size_1; + const int slice_start = *(p_slicings + (thread_id * 2)); + const int slice_end = *(p_slicings + (thread_id * 2) + 1); + + for (int i = 0; i < slice_end - slice_start; i++) + { + *(p_return_value_row + i) = *(p_input + slice_start + i); + } + } +} + +/** + * @brief Intended to be used on the output of + * extract_slicings_cuda_kernel. It reformats + * extraextracted_slices row-wise to merge-pairs + * format. E.g. + * row_i = [1, 2, 3] -> [[1,2], [1,3]] + * + * @param extracted_slices + * @param extracted_slices_size_0 + * @param extracted_slices_size_1 + * @param lengths + * @param row_offset_for_thread + * @param return_value + */ +__global__ void format_extracted_sorted_slicings_to_merge_pairs_kernel( + int64_t *extracted_slices, + int64_t extracted_slices_size_0, + int64_t extracted_slices_size_1, + int64_t *lengths, + int64_t *row_offset_for_thread, + int64_t *return_value) +{ + const int thread_id = blockIdx.x * blockDim.x + threadIdx.x; + + if (thread_id < extracted_slices_size_0) + { + const int length = *(lengths + thread_id); + const int row_offset = (thread_id > 0) ? *(row_offset_for_thread + thread_id - 1) : 0; + auto extracted_slices_row = extracted_slices + thread_id * extracted_slices_size_1; + auto const first_col_value = *extracted_slices_row; + + auto return_value_row = return_value + 2 * row_offset; + for (int i = 0; i < length - 1; i++) + { + *(return_value_row) = first_col_value; + *(return_value_row + 1) = *(extracted_slices_row + i + 1); + return_value_row = return_value_row + 2; + } + } +} + +} //namespace + +Tensor sorted_pivot_indices_to_merge_pairs_cuda_kernel_call( + const Tensor & input, + const Tensor & slicings) +{ + // ASSERTION input.dtype() == int64 + // ASSERTION slicings.dtype() == int64 + // ASSERTION all(input.ge(0)) + // ASSERTION all(slicings.ge(0)) + // ASSERTION all(slicings[:, 0].leq(slicings[:, 1])) + // ASSERTION slicings[:, 1].max() < input.size(0) + + CHECK_SAME_DEVICE(input, slicings); + at::OptionalDeviceGuard guard(device_of(input)); + + auto lengths = (slicings.slice(1, 1, 2) - slicings.slice(1, 0, 1)).contiguous(); + auto max_lengths = lengths.max().item(); + + Tensor extracted_slicings = torch::empty( + {slicings.size(0), max_lengths}, + input.options()); + extracted_slicings.fill_(std::numeric_limits::max()); + + const int threads_per_block_apply_slicings = 256; + const int blocks_apply_slicings = slicings.size(0) / threads_per_block_apply_slicings + 1; + extract_slicings_cuda_kernel<<>>( + input.data(), + slicings.data(), + extracted_slicings.data(), + extracted_slicings.size(0), + extracted_slicings.size(1)); + + cudaCheckError(); + + auto extracted_slicings_sorted = std::get<0>(extracted_slicings.sort(1)).contiguous(); + + auto lengths_minus_1 = lengths - 1; + auto row_offset_for_thread = lengths_minus_1.cumsum(0); + + auto merge_pairings_size_0 = row_offset_for_thread[-1][0].item(); + auto merge_pairings = torch::empty({merge_pairings_size_0, 2}, input.options()); + merge_pairings.fill_(-1); + + const int threads_per_block = 256; + const int blocks = extracted_slicings_sorted.size(0) / threads_per_block + 1; + + format_extracted_sorted_slicings_to_merge_pairs_kernel<<>>( + extracted_slicings_sorted.data(), + extracted_slicings_sorted.size(0), + extracted_slicings_sorted.size(1), + lengths.data(), + row_offset_for_thread.data(), + merge_pairings.data()); + + cudaCheckError(); + + return merge_pairings; +} + +Tensor find_merge_pairings( + const Tensor & pivots, + int64_t max_pairs = -1) +{ + + CHECK_TENSOR_CUDA_CONTIGUOUS(pivots); + CHECK_TENSOR_INT64(pivots); + CHECK_GREATER_EQ(pivots.size(0), 1); + + // If max_pairs < 1 we do not want to restrict the maximum number of + // mergings per iteration ... + if (max_pairs < 1) + { + max_pairs = std::numeric_limits::max(); + } + + auto sort_res = pivots.sort(0); + auto sort_val = std::get<0>(sort_res); + auto sort_ind = std::get<1>(sort_res); + + auto slicings = find_slicing_indices_cuda_kernel_call(sort_val).contiguous(); + + Tensor merge_pairs; + if (slicings.size(0) != 0) + { + + merge_pairs = sorted_pivot_indices_to_merge_pairs_cuda_kernel_call(sort_ind, slicings); + // We sort the pairs such that pairs with smaller index come first. + // This improves performance??? + if (merge_pairs.size(0) > max_pairs) + { + + sort_res = merge_pairs.slice(1, 0, 1).sort(0); + sort_ind = std::get<1>(sort_res); + sort_ind = sort_ind.slice(0, 0, max_pairs).squeeze(); + + merge_pairs = merge_pairs.index_select(0, sort_ind); + merge_pairs = merge_pairs.contiguous(); + } + } + else + { + merge_pairs = torch::empty({0, 2}, pivots.options()); + } + + return merge_pairs; +} + +#pragma endregion + +#pragma region merge_columns + +namespace +{ + +template +__device__ void merge_one_column_s( + scalar_t *p_merger, + scalar_t *p_target, // the position of the target column, set to -1 + scalar_t *p_target_cache, // contains the copied values of target column + int64_t boundary_array_size_1, + int *d_boundary_array_needs_resize) +{ + // Assertion: comp_desc_sort_ba[:, -1] == -1 + + int p_target_increment_count = 0; + + while (true) + { + if (*p_merger == -1 && *p_target_cache == -1) + { + // both are -1, we have reached the end of meaningful entries -> break + break; + } + + if (*p_merger == *p_target_cache) + { + // both values are the same but not -1 -> we eliminate + p_target_cache++; + p_merger++; + } + else + { + + if (*p_merger > *p_target_cache) + { + //merger value is greater -> we take it + *p_target = *p_merger; + p_merger++; + } + else + { + //target value is greate -> we take it + *p_target = *p_target_cache; + p_target_cache++; + } + + p_target++; + p_target_increment_count += 1; + } + } + + if (p_target_increment_count > boundary_array_size_1 / 2) + { + *d_boundary_array_needs_resize = 1; + } +} + +template +__global__ void merge_columns_cuda_kernel( + scalar_t *comp_desc_sort_ba, + size_t descending_sorted_boundary_array_size_1, + scalar_t *cache, + int64_t *merge_pairings, + size_t merge_pairings_size_0, + int *d_boundary_array_needs_resize) +{ + //ASSERTION: cache.size(1) == comp_desc_sort_ba.size(1) + const int thread_id = blockIdx.x * blockDim.x + threadIdx.x; + + if (thread_id < merge_pairings_size_0) + { + + const int filt_id_merger = merge_pairings[thread_id * 2]; + const int filt_id_target = merge_pairings[thread_id * 2 + 1]; + + merge_one_column_s( + comp_desc_sort_ba + (filt_id_merger * descending_sorted_boundary_array_size_1), + comp_desc_sort_ba + (filt_id_target * descending_sorted_boundary_array_size_1), + cache + (thread_id * descending_sorted_boundary_array_size_1), + descending_sorted_boundary_array_size_1, + d_boundary_array_needs_resize); + } +} + +} //namespace + +template +void merge_columns_cuda_kernel_call( + Tensor & comp_desc_sort_ba, + const Tensor & merge_pairings, + int *h_boundary_array_needs_resize) +{ + CHECK_SAME_DEVICE(comp_desc_sort_ba, merge_pairings); + at::OptionalDeviceGuard guard(device_of(comp_desc_sort_ba)); + + const int threads_per_block = 32; + const int blocks = merge_pairings.size(0) / threads_per_block + 1; + + auto targets = merge_pairings.slice(1, 1).squeeze(); + + // fill cache for merging ... + // TODO optimize: we do not need all columns it is enough to take des...array.size(1)/2 + 1 + // ATTENTION if we do this we have to inform merge_columns_cuda_kernel about this!!! + auto cache = comp_desc_sort_ba.index_select(0, targets); + + auto size = sizeof(int); + int *d_boundary_array_needs_resize; + cudaMalloc(&d_boundary_array_needs_resize, size); + cudaMemcpy(d_boundary_array_needs_resize, h_boundary_array_needs_resize, size, cudaMemcpyHostToDevice); + + // reset content of target columns + comp_desc_sort_ba.index_fill_(0, targets, -1); + + merge_columns_cuda_kernel<<>>( + comp_desc_sort_ba.data(), + comp_desc_sort_ba.size(1), + cache.data(), + merge_pairings.data(), + merge_pairings.size(0), + d_boundary_array_needs_resize); + + cudaCheckError(); + + cudaMemcpy(h_boundary_array_needs_resize, d_boundary_array_needs_resize, size, cudaMemcpyDeviceToHost); + cudaCheckError(); + + cudaFree(d_boundary_array_needs_resize); + cudaCheckError(); +} + +Tensor resize_boundary_array( + const Tensor & comp_desc_sort_ba) +{ + auto tmp = torch::empty_like(comp_desc_sort_ba); + tmp.fill_(-1); + auto new_ba = cat(TensorList({comp_desc_sort_ba, tmp}), 1); + return new_ba.contiguous(); +} + +Tensor merge_columns( + const Tensor & comp_desc_sort_ba, + const Tensor & merge_pairings) +{ + CHECK_TENSOR_CUDA_CONTIGUOUS(comp_desc_sort_ba); + CHECK_TENSOR_INT64(comp_desc_sort_ba); + CHECK_TENSOR_CUDA_CONTIGUOUS(merge_pairings); + CHECK_TENSOR_INT64(merge_pairings); + + auto ba = comp_desc_sort_ba; + int boundary_array_needs_resize = 0; + int *h_boundary_array_needs_resize = &boundary_array_needs_resize; + + merge_columns_cuda_kernel_call( + ba, + merge_pairings, + h_boundary_array_needs_resize); + + if (*h_boundary_array_needs_resize == 1) + { + ba = resize_boundary_array(ba); + } + + return ba; +} + +#pragma endregion + +#pragma region read_barcodes + +std::vector> read_barcodes( + const Tensor & pivots, + const Tensor & simplex_dimension, + int64_t max_dim_to_read_of_reduced_ba) +{ + + CHECK_TENSOR_CUDA_CONTIGUOUS(pivots); + CHECK_TENSOR_INT64(pivots); + CHECK_TENSOR_CUDA_CONTIGUOUS(simplex_dimension); + CHECK_TENSOR_INT64(simplex_dimension); + + CHECK_EQUAL(simplex_dimension.dim(), 1); + CHECK_GREATER_EQ(simplex_dimension.size(0), 1); + + std::vector ret_non_ess; + std::vector ret_ess; + auto simp_dim = simplex_dimension.unsqueeze(1); + + auto range = torch::empty_like(pivots); + TensorUtils::fill_range_cuda_(range); + + auto pool_for_barcodes_non_essential = cat({pivots, range}, 1); + auto mask_pivot = pivots.ge(0); + + // all dimenions mask non essential ... + auto mask_non_essential = mask_pivot.expand({-1, 2}); + + // all dimensions mask essential ... + auto mask_no_pivot = pivots.le(-1); + auto mask_rows_with_no_lowest_one = torch::ones_like(mask_no_pivot); + auto row_indices_with_lowest_one = pivots.masked_select(mask_pivot).toType(ScalarType::Long); + + if (row_indices_with_lowest_one.numel() != 0){ + mask_rows_with_no_lowest_one.index_fill_(0, row_indices_with_lowest_one, 0); + } + + auto mask_ess = mask_no_pivot.__and__(mask_rows_with_no_lowest_one); + + for (int dim = 0; dim <= max_dim_to_read_of_reduced_ba; dim++) + { + + // non essentials ... + auto mask_dim = simp_dim.eq(dim + 1); + auto mask_non_essential_dim = mask_non_essential.__and__(mask_dim.expand({-1, 2})); + auto barcodes_non_essential_dim = pool_for_barcodes_non_essential.masked_select(mask_non_essential_dim).view({-1, 2}); + + ret_non_ess.push_back(barcodes_non_essential_dim); + + // essentials ... + auto mask_dim_ess = simp_dim.eq(dim); + auto mask_essential_dim = mask_ess.__and__(mask_dim_ess); + auto barcode_birth_times_essential_dim = range.masked_select(mask_essential_dim).view({-1, 1}); + + ret_ess.push_back(barcode_birth_times_essential_dim); + } + + return std::vector>({ret_non_ess, ret_ess}); +} + +#pragma endregion + +std::vector> calculate_persistence( + const Tensor & comp_desc_sort_ba, + const Tensor & ind_not_reduced, //TODO rename parameter accordingly to python binding + const Tensor & simplex_dimension, + int64_t max_dim_to_read_of_reduced_ba, + int64_t max_pairs = -1) +{ + + CHECK_TENSOR_CUDA_CONTIGUOUS(comp_desc_sort_ba); + CHECK_TENSOR_INT64(comp_desc_sort_ba); + CHECK_TENSOR_CUDA_CONTIGUOUS(ind_not_reduced); + CHECK_TENSOR_INT64(ind_not_reduced); + CHECK_TENSOR_CUDA_CONTIGUOUS(simplex_dimension); + CHECK_TENSOR_INT64(simplex_dimension); + + CHECK_TENSOR_INT64(comp_desc_sort_ba); + CHECK_TENSOR_INT64(ind_not_reduced); + CHECK_TENSOR_INT64(simplex_dimension); + + if (comp_desc_sort_ba.numel() != 0){ + CHECK_EQUAL(comp_desc_sort_ba.dim(), 2); + CHECK_SMALLER_EQ(comp_desc_sort_ba.size(0), simplex_dimension.size(0)); + + CHECK_EQUAL((simplex_dimension.max().item()+1)*2, comp_desc_sort_ba.size(1)); + } + + auto ba = comp_desc_sort_ba; + auto ind_not_red = ind_not_reduced; + auto simp_dim = simplex_dimension; + + int iterations = 0; + + Tensor mask_not_reduced, pivots, merge_pairings, new_ind_not_red; + bool continue_loop = true; + + // Empty initial compressed boundary array means there are just + // simplices in the complex. Hence we need no reduction ... + if (ba.numel() == 0){ + continue_loop = false; + } + + while (continue_loop) + { + // PRINT(ba); + pivots = ba.slice(1, 0, 1).contiguous(); + + merge_pairings = find_merge_pairings(pivots, max_pairs); + + if (merge_pairings.size(0) == 0){ + break; + } + // PRINT(merge_pairings); + + ba = merge_columns(ba, merge_pairings); + + new_ind_not_red = torch::empty({ba.size(0), 1}, ba.options().dtype(torch::kInt64)); + + TensorUtils::fill_range_cuda_(new_ind_not_red); + + pivots = ba.slice(1, 0, 1).contiguous(); + mask_not_reduced = pivots.ge(0); + new_ind_not_red = new_ind_not_red.masked_select(mask_not_reduced).contiguous(); + + ba = ba.index_select(0, new_ind_not_red).contiguous(); + + ind_not_red = ind_not_red.index_select(0, new_ind_not_red); + + iterations++; + } + + //std::cout << "Reached end of reduction after " << iterations << " iterations" << std::endl; + + auto real_pivots = torch::empty({simp_dim.size(0), 1}, ba.options()).fill_(-1); + + if (ba.numel() != 0){ + real_pivots.index_copy_(0, ind_not_red, pivots); + } + auto barcodes = read_barcodes(real_pivots, simp_dim, max_dim_to_read_of_reduced_ba); + + return barcodes; +} + +} // namespace CalcPersCuda diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/calc_pers_cuda.cuh b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/calc_pers_cuda.cuh new file mode 100644 index 0000000..6494138 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/calc_pers_cuda.cuh @@ -0,0 +1,32 @@ +#pragma once + + +#include + + +using namespace torch; + +namespace CalcPersCuda +{ + +Tensor find_merge_pairings( + const Tensor & pivots, + int64_t max_pairs = -1); + +void merge_columns( + const Tensor & compr_desc_sort_ba, + const Tensor & merge_pairs); + +std::vector> read_barcodes( + const Tensor & pivots, + const Tensor & simplex_dimension, + int64_t max_dim_to_read_of_reduced_ba); + +std::vector> calculate_persistence( + const Tensor & compr_desc_sort_ba, + const Tensor & ba_row_i_to_bm_col_i, + const Tensor & simplex_dimension, + int64_t max_dim_to_read_of_reduced_ba, + int64_t max_pairs); + +} // namespace CalcPersCuda \ No newline at end of file diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/cuda_checks.cuh b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/cuda_checks.cuh new file mode 100644 index 0000000..67baabb --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/cuda_checks.cuh @@ -0,0 +1,13 @@ +#pragma once + + +#include + + +#define cudaCheckError() { \ + cudaError_t e=cudaGetLastError(); \ + if(e!=cudaSuccess) { \ + printf("Cuda failure %s:%d: '%s'\n",__FILE__,__LINE__,cudaGetErrorString(e)); \ + exit(0); \ + } \ +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/a.out b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/a.out new file mode 100755 index 0000000000000000000000000000000000000000..79c66dd765c5af4642c60c6d7b62e751d30ca290 GIT binary patch literal 74960 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zPWj7#KMwy@e)T;<1y?eGWF+0G{QR?0Mip>WWtCrjuT4SqJwqfBms9`W0+zlNsPe1t zx+%y%YlSbw$xEeE@D`xy+k@&^eg93t<(!Y?Cpne;j5gYELpoLY)pz0)RKEkN%2)YS z{qN@d)0m<9o}7YfxWbhGRQz}5zYjP{qWI${g5<@J>|4K_?Y|a8J-?6hnUKp@JRIze zUpn%u@7O7*(tG9KA +#include +#include "../utils/macros.hpp" + +/** + * @brief Binary Indexed Tree + */ +template +struct binary_indexed_tree { + typedef typename CommutativeMonoid::value_type value_type; + CommutativeMonoid mon; + std::vector data; + binary_indexed_tree(int n, CommutativeMonoid const & mon_ = CommutativeMonoid()) + : mon(mon_), + data(n, mon.unit()) { + } + template + binary_indexed_tree(InputIterator first, InputIterator last, CommutativeMonoid const & mon_ = CommutativeMonoid()) + : mon(mon_), + data(first, last) { + REP3 (j, 1, data.size() + 1) { + int k = j + (j & -j); + if (k - 1 < static_cast(data.size())) { + data[k - 1] = mon.mult(data[k - 1], data[j - 1]); + } + } + } + /** + * @note $a_i \gets a_i + z$ + * @note $O(\log N)$ + */ + void point_mult(int i, value_type z) { + assert (0 <= i and i < static_cast(data.size())); + for (int j = i + 1; j <= static_cast(data.size()); j += j & -j) { + data[j - 1] = mon.mult(data[j - 1], z); + } + } + /** + * @note $\sum _ {i \lt r} a_i$ + * @note $O(\log N)$ + */ + value_type initial_range_get(int r) { + assert (0 <= r and r <= static_cast(data.size())); + value_type acc = mon.unit(); + for (int i = r; 0 < i; i -= i & -i) { + acc = mon.mult(data[i - 1], acc); + } + return acc; + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/binary_indexed_tree.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/binary_indexed_tree.test.cpp new file mode 100644 index 0000000..84d3222 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/binary_indexed_tree.test.cpp @@ -0,0 +1,27 @@ +#define PROBLEM "https://judge.yosupo.jp/problem/point_add_range_sum" +#include +#include +#include +#include "../data_structure/binary_indexed_tree.hpp" +#include "../monoids/plus.hpp" +#include "../utils/macros.hpp" +using namespace std; + +int main() { + int n, q; scanf("%d%d", &n, &q); + vector a(n); + REP (i, n) { + scanf("%lld", &a[i]); + } + binary_indexed_tree > bit(ALL(a)); + while (q --) { + int t, x, y; scanf("%d%d%d", &t, &x, &y); + if (t == 0) { + bit.point_mult(x, y); + } else if (t == 1) { + long long answer = bit.initial_range_get(y) - bit.initial_range_get(x); + printf("%lld\n", answer); + } + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/convex_hull_trick.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/convex_hull_trick.hpp new file mode 100644 index 0000000..6da4d4b --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/convex_hull_trick.hpp @@ -0,0 +1,109 @@ +#pragma once +#include +#include +#include +#include +#include +#include + + +namespace convex_hull_trick_details { + /** + * @note y = ax + b + */ + struct line_t { int64_t a, b; }; + bool operator < (line_t lhs, line_t rhs) { + return std::make_pair(- lhs.a, lhs.b) < std::make_pair(- rhs.a, rhs.b); + } + + struct rational_t { int64_t num, den; }; + bool operator < (rational_t lhs, rational_t rhs) { + if (lhs.num == INT64_MAX or rhs.num == - INT64_MAX) return false; + if (lhs.num == - INT64_MAX or rhs.num == INT64_MAX) return true; + return (__int128_t)lhs.num * rhs.den < (__int128_t)rhs.num * lhs.den; + } +} + + +/* + * @brief Convex Hull Trick (非単調, online) + * @docs data_structure/convex_hull_trick.md + */ +class convex_hull_trick { + typedef convex_hull_trick_details::line_t line_t; + typedef convex_hull_trick_details::rational_t rational_t; + static rational_t make_rational(int64_t num, int64_t den = 1) { + if (den < 0) { num *= -1; den *= -1; } + return { num, den }; // NOTE: no reduction + } + +public: + convex_hull_trick() { + lines.insert({ + INT64_MAX, 0 }); // sentinels + lines.insert({ - INT64_MAX, 0 }); + cross.emplace(make_rational(- INT64_MAX), (line_t) { - INT64_MAX, 0 }); + } + /** + * @note O(log n) + */ + void add_line(int64_t a, int64_t b) { + auto it = lines.insert({ a, b }).first; + if (not is_required(*prev(it), { a, b }, *next(it))) { + lines.erase(it); + return; + } + cross.erase(cross_point(*prev(it), *next(it))); + { // remove right lines + auto ju = prev(it); + while (ju != lines.begin() and not is_required(*prev(ju), *ju, { a, b })) -- ju; + cross_erase(ju, prev(it)); + it = lines.erase(++ ju, it); + } + { // remove left lines + auto ju = next(it); + while(next(ju) != lines.end() and not is_required({ a, b }, *ju, *next(ju))) ++ ju; + cross_erase(++ it, ju); + it = prev(lines.erase(it, ju)); + } + cross.emplace(cross_point(*prev(it), *it), *it); + cross.emplace(cross_point(*it, *next(it)), *next(it)); + assert (not empty()); + } + bool empty() const { + return lines.size() <= 2; + } + /** + * @note O(log n) + */ + int64_t get_min(int64_t x) const { + assert (not empty()); + line_t f = prev(cross.lower_bound(make_rational(x)))->second; + return f.a * x + f.b; + } + +private: + std::set lines; + std::map cross; + template + void cross_erase(Iterator first, Iterator last) { + for (; first != last; ++ first) { + cross.erase(cross_point(*first, *next(first))); + } + } + rational_t cross_point(line_t f1, line_t f2) const { + if (f1.a == INT64_MAX) return make_rational(- INT64_MAX); + if (f2.a == - INT64_MAX) return make_rational( INT64_MAX); + return make_rational(f1.b - f2.b, f2.a - f1.a); + } + bool is_required(line_t f1, line_t f2, line_t f3) const { + if (f1.a == f2.a and f1.b <= f2.b) return false; + if (f1.a == INT64_MAX or f3.a == - INT64_MAX) return true; + return (__int128_t)(f2.a - f1.a) * (f3.b - f2.b) < (__int128_t)(f2.b - f1.b) * (f3.a - f2.a); + } +}; + +struct inverted_convex_hull_trick { + convex_hull_trick data; + void add_line(int64_t a, int64_t b) { data.add_line(- a, - b); } + int64_t get_max(int64_t x) { return - data.get_min(x); } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/convex_hull_trick.line_add_get_min.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/convex_hull_trick.line_add_get_min.test.cpp new file mode 100644 index 0000000..0ebf015 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/convex_hull_trick.line_add_get_min.test.cpp @@ -0,0 +1,26 @@ +#define PROBLEM "https://judge.yosupo.jp/problem/line_add_get_min" +#include "../data_structure/convex_hull_trick.hpp" +#include "../utils/macros.hpp" +#include + +int main() { + int n, q; scanf("%d%d", &n, &q); + convex_hull_trick cht; + REP (i, n) { + int64_t a, b; scanf("%lld%lld", &a, &b); + cht.add_line(a, b); + } + while (q --) { + int t; scanf("%d", &t); + if (t == 0) { + int64_t a, b; scanf("%lld%lld", &a, &b); + cht.add_line(a, b); + } else if (t == 1) { + int64_t p; scanf("%lld", &p); + printf("%lld\n", cht.get_min(p)); + } else { + assert (false); + } + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/convex_hull_trick.md b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/convex_hull_trick.md new file mode 100644 index 0000000..7d5cb50 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/convex_hull_trick.md @@ -0,0 +1,6 @@ +## 概要 + +一次関数の集合 $F = \emptyset \subseteq \lbrace \lambda x. ax + b \mid a, b \in \mathbb{Z} \rbrace$ に対し、次が $O(\log N)$ amortized (一部は $O(\log N)$) で処理可能: + +- $\mathtt{add\unicode{95}line}(f)$: $F \gets F \cup \lbrace f \rbrace$ と更新する。 +- $\mathtt{get\unicode{95}min}(x)$: 最小値 $\min _ {f \in F} f(x)$ を計算する。 diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/dual_segment_tree.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/dual_segment_tree.hpp new file mode 100644 index 0000000..482f330 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/dual_segment_tree.hpp @@ -0,0 +1,47 @@ +#pragma once +#include +#include +#include + +/** + * @brief Dual Segment Tree / 双対セグメント木 (monoids, 完全二分木) + * @docs data_structure/dual_segment_tree.md + * @tparam Monoid (commutativity is not required) + */ +template +struct dual_segment_tree { + typedef typename Monoid::value_type value_type; + const Monoid mon; + int n; + std::vector f; + dual_segment_tree() = default; + dual_segment_tree(int n_, const Monoid & mon_ = Monoid()) : mon(mon_) { + n = 1; while (n < n_) n *= 2; + f.resize(2 * n - 1, mon.unit()); + } + value_type point_get(int i) { // 0-based + assert (0 <= i and i < n); + value_type acc = mon.unit(); + for (i += n; i > 0; i /= 2) { // 1-based + acc = mon.mult(f[i - 1], acc); + } + return acc; + } + void range_apply(int l, int r, value_type g) { // 0-based, [l, r) + assert (0 <= l and l <= r and r <= n); + range_apply(0, 0, n, l, r, g); + } + void range_apply(int i, int il, int ir, int l, int r, value_type g) { + if (l <= il and ir <= r) { // 0-based + f[i] = mon.mult(g, f[i]); + } else if (ir <= l or r <= il) { + // nop + } else { + range_apply(2 * i + 1, il, (il + ir) / 2, 0, n, f[i]); + range_apply(2 * i + 2, (il + ir) / 2, ir, 0, n, f[i]); + f[i] = mon.unit(); + range_apply(2 * i + 1, il, (il + ir) / 2, l, r, g); + range_apply(2 * i + 2, (il + ir) / 2, ir, l, r, g); + } + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/dual_segment_tree.md b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/dual_segment_tree.md new file mode 100644 index 0000000..108518b --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/dual_segment_tree.md @@ -0,0 +1,6 @@ +## 概要 + +monoid $F = (F, \circ, \mathrm{id})$ の要素の列 $f = (f_0, f_1, \dots, f _ {n - 1}) \in F^n$ に対し、次が $O(\log N)$ で処理可能: + +- $\mathtt{range\unicode{95}set}(l, r, g)$: それぞれの $i \in [l, r)$ に対し $f_i \gets g \circ f_i$ と更新する。 +- $\mathtt{point\unicode{95}get}(i)$: 値 $f_i$ を計算する。 diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/dual_segment_tree.range_update_query.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/dual_segment_tree.range_update_query.test.cpp new file mode 100644 index 0000000..a3c736c --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/dual_segment_tree.range_update_query.test.cpp @@ -0,0 +1,24 @@ +#define PROBLEM "http://judge.u-aizu.ac.jp/onlinejudge/description.jsp?id=DSL_2_D" +#include "../data_structure/dual_segment_tree.hpp" +#include "../monoids/left.hpp" + +#include +using namespace std; + +int main() { + int n, q; cin >> n >> q; + dual_segment_tree > segtree(n); + segtree.range_apply(0, n, make_pair(true, (1u << 31) - 1)); + while (q --) { + int com; cin >> com; + if (com == 0) { + int l, r; uint32_t x; cin >> l >> r >> x; + ++ r; + segtree.range_apply(l, r, make_pair(true, x)); + } else if (com == 1) { + int i; cin >> i; + cout << segtree.point_get(i).second << endl; + } + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/dynamic_connectivity_offline.dynamic_graph_vertex_add_component_sum.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/dynamic_connectivity_offline.dynamic_graph_vertex_add_component_sum.test.cpp new file mode 100644 index 0000000..5e934ef --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/dynamic_connectivity_offline.dynamic_graph_vertex_add_component_sum.test.cpp @@ -0,0 +1,44 @@ +#define PROBLEM "https://judge.yosupo.jp/problem/dynamic_graph_vertex_add_component_sum" +#include "../data_structure/dynamic_connectivity_offline.hpp" +#include "../monoids/plus.hpp" +#include "../hack/fastio.hpp" +#include "../utils/macros.hpp" + +int main() { + int n = in(); + int q = in(); + + dynamic_connectivity_offline > dycon(n); + REP (v, n) { + int64_t a = in(); + dycon.point_set(v, a); + } + REP (i, q) { + int t = in(); + if (t == 0) { + int u = in(); + int v = in(); + dycon.link(u, v); + } else if (t == 1) { + int u = in(); + int v = in(); + dycon.cut(u, v); + } else if (t == 2) { + int v = in(); + int64_t x = in(); + dycon.point_mult(v, x); + } else if (t == 3) { + int v = in(); + dycon.component_get(v); + } else { + assert (false); + } + } + + std::vector answer = dycon.run(); + for (int64_t a : answer) { + out(a); + out('\n'); + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/dynamic_connectivity_offline.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/dynamic_connectivity_offline.hpp new file mode 100644 index 0000000..0e5abb5 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/dynamic_connectivity_offline.hpp @@ -0,0 +1,162 @@ +#pragma once +#include +#include +#include +#include +#include +#include +#include "../data_structure/union_find_tree_foldable_undoable.hpp" +#include "../data_structure/reporting_segment_tree.hpp" +#include "../utils/macros.hpp" + +/** + * @brief Dynamic Connectivity (offline, commutative monoids) + * @docs data_structure/dynamic_connectivity_offline.md + */ +template +class dynamic_connectivity_offline { + typedef typename CommutativeMonoid::value_type value_type; + enum query_tag { + LINK, + ADD, + }; + struct query_type { + query_tag tag; + int l, r; + int a; + int64_t b; + }; + + const CommutativeMonoid mon; + int size; + int time; + std::vector set_queries; + std::vector > get_queries; + std::map, int> current_edges; + std::vector > > current_values; + + void flush_time() { + if (not get_queries.empty() and get_queries.back().first == time) { + ++ time; + } + } + +public: + /** + * @arg size is the number of vertices + */ + dynamic_connectivity_offline(int size_, const CommutativeMonoid & mon_ = CommutativeMonoid()) + : mon(mon_) { + size = size_; + time = 0; + current_values.resize(size); + } + void link(int x, int y) { + flush_time(); + auto edge = std::minmax({ x, y }); + assert (not current_edges.count(edge)); + current_edges[edge] = time; + } + void cut(int x, int y) { + flush_time(); + auto edge = std::minmax({ x, y }); + assert (current_edges.count(edge)); + set_queries.emplace_back((query_type) { + LINK, + current_edges[edge], + time, + edge.first, + edge.second, + }); + current_edges.erase(edge); + } + void point_set(int x, value_type value) { + flush_time(); + while (not current_values[x].empty()) { + auto [l, value_] = current_values[x].back(); + current_values[x].pop_back(); + set_queries.emplace_back((query_type) { + ADD, + l, + time, + x, + value_, + }); + } + current_values[x].emplace_back(time, value); + } + void point_mult(int x, value_type value) { + flush_time(); + current_values[x].emplace_back(time, value); + } + void component_get(int x) { + get_queries.emplace_back(time, x); + } + + /** + * @note $O(Q \log Q)$ + */ + std::vector run() { + std::vector results; + auto it = get_queries.begin(); + + // close half-open queries + int current_size = set_queries.size(); + for (auto [edge, l] : current_edges) { + set_queries.emplace_back((query_type) { + LINK, + l, + time + 1, + edge.first, + edge.second, + }); + } + REP (x, size) { + for (auto [l, value] : current_values[x]) { + set_queries.emplace_back((query_type) { + ADD, + l, + time + 1, + x, + value, + }); + } + } + + // prepare undoable union-find tree + union_find_tree_foldable_undoable uft(size, mon); + auto add = [&](int i) { + auto & q = set_queries[i]; + if (q.tag == LINK) { + uft.unite_trees(q.a, q.b); + } else if (q.tag == ADD) { + uft.tree_set(q.a, mon.mult(uft.tree_get(q.a), q.b)); + } else { + assert (false); + } + }; + auto remove = [&](int i) { + uft.undo(); + }; + auto report = [&](int t) { + for (; it != get_queries.end() and it->first == t; ++ it) { + int x = it->second; + results.push_back(uft.tree_get(x)); + } + }; + + // use segment trees on time-axis for queries + reporting_segment_tree segtree(time + 1); + REP (i, set_queries.size()) { + auto & q = set_queries[i]; + segtree.add_segment(q.l, q.r, i); + } + segtree.traverse_segments(add, remove, report); + + // re-open closed queries + set_queries.resize(current_size); + + assert (it == get_queries.end()); + return results; + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/dynamic_connectivity_offline.md b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/dynamic_connectivity_offline.md new file mode 100644 index 0000000..a3a82b6 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/dynamic_connectivity_offline.md @@ -0,0 +1,11 @@ +## Operations + +単純無向グラフ $G = (V, E)$ であって頂点 $x \in V$ に可換 monoid $M = (M, +, 0)$ による重み $a_x \in M$ が付いたものに対し、次のクエリが offline で全体で $O(Q \log Q)$ で処理可能: + +- $\mathtt{link}(x, y)$: 頂点 $x, y$ 間に辺 $\lbrace x, y \rbrace$ を追加する。 +- $\mathtt{cut}(x, y)$: 頂点 $x, y$ 間から辺 $\lbrace x, y \rbrace$ を削除する。 +- $\mathtt{point\unicode{95}set}(x, b)$: 頂点 $x$ の重みを $a_x \gets b$ と更新する。 +- $\mathtt{point\unicode{95}mult}(x, b)$: 頂点 $x$ の重みを $a_x \gets a_x + b$ と更新する。 +- $\mathtt{component\unicode{95}get}(x)$: 頂点 $x$ と同じ連結成分 $C \subseteq V$ に所属する頂点の重みの総和 $\sum _ {x \in C} a_x$ を計算する。 + +ただし offline であるので、計算結果は最後に $\mathtt{run}()$ を呼ぶことで取得する。 diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/euler_tour_subtree_query.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/euler_tour_subtree_query.hpp new file mode 100644 index 0000000..5d2cb35 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/euler_tour_subtree_query.hpp @@ -0,0 +1,48 @@ +#pragma once +#include +#include +#include "../utils/macros.hpp" +#include "../graph/euler_tour_preorder.hpp" +#include "../data_structure/segment_tree.hpp" + +/** + * @brief Euler Tour (subtree queries, with commutative monoids) + * @docs data_structure/euler_tour_subtree_query.md + */ +template +class euler_tour_subtree_query { + typedef typename CommutativeMonoid::value_type value_type; + segment_tree data; + std::vector left, right; + +public: + euler_tour_subtree_query(const std::vector > & g, int root, const CommutativeMonoid & mon_ = CommutativeMonoid()) + : data(g.size(), mon_) { + std::vector tour; + do_euler_tour_preorder(g, root, tour, left, right); + } + template + euler_tour_subtree_query(const std::vector > & g, int root, InputIterator first, InputIterator last, const CommutativeMonoid & mon_ = CommutativeMonoid()) + : data(std::distance(first, last), mon_) { + assert ((int)g.size() == std::distance(first, last)); + std::vector tour; + do_euler_tour_preorder(g, root, tour, left, right); + REP (x, g.size()) { + data.unsafe_point_set(left[x], *(first ++)); + } + data.unsafe_rebuild(); + } + + void vertex_set(int x, value_type a) { + assert (0 <= x and x < (int)left.size()); + return data.point_set(left[x], a); + } + value_type vertex_get(int x) { + assert (0 <= x and x < (int)left.size()); + return data.point_get(left[x]); + } + value_type subtree_get(int x) { + assert (0 <= x and x < (int)left.size()); + return data.range_get(left[x], right[x]); + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/euler_tour_subtree_query.md b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/euler_tour_subtree_query.md new file mode 100644 index 0000000..72f8bbc --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/euler_tour_subtree_query.md @@ -0,0 +1,7 @@ +## Operations + +根付き木 $T$ であってそれぞれの頂点 $x$ に可換 monoid 重み $a_x \in M$ が乗ったものがあるとする。頂点数を $N$ として均し $O(\log N)$ で次が処理できる。 + +- $\mathtt{vertex\unicode{95}set}(x, b)$: 頂点 $x$ の重みを $a_x \gets b$ と更新する。 +- $\mathtt{vertex\unicode{95}get}(x)$: 頂点 $x$ の重み $a_x$ を計算する。 +- $\mathtt{subtree\unicode{95}get}(x, y)$: 頂点 $x$ を根とする部分木に属する頂点の重みの総和 $$\sum _ {y ~\text{is a descendent of}~ x} a_y$$ を計算する。 diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/euler_tour_subtree_query.vertex_add_subtree_sum.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/euler_tour_subtree_query.vertex_add_subtree_sum.test.cpp new file mode 100644 index 0000000..7af07ac --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/euler_tour_subtree_query.vertex_add_subtree_sum.test.cpp @@ -0,0 +1,41 @@ +#define PROBLEM "https://judge.yosupo.jp/problem/vertex_add_subtree_sum" +#include "../data_structure/euler_tour_subtree_query.hpp" +#include "../monoids/plus.hpp" +#include "../utils/macros.hpp" +#include "../hack/fastio.hpp" +#include +using namespace std; + +int main() { + int n = in(); + int q = in(); + + // initialize + vector a(n); + REP (i, n) { + a[i] = in(); + } + vector > g(n); + REP3 (i, 1, n) { + int p_i = in(); + g[i].push_back(p_i); + g[p_i].push_back(i); + } + constexpr int root = 0; + euler_tour_subtree_query > euler(g, root, ALL(a)); + + // answer to queries + while (q --) { + int t = in(); + if (t == 0) { + int u = in(); + int64_t x = in(); + euler.vertex_set(u, euler.vertex_get(u) + x); + } else if (t == 1) { + int u = in(); + out(euler.subtree_get(u)); + out('\n'); + } + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/fully_indexable_dictionary.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/fully_indexable_dictionary.hpp new file mode 100644 index 0000000..a45b6c2 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/fully_indexable_dictionary.hpp @@ -0,0 +1,90 @@ +#pragma once +#include +#include +#include +#include +#include "../utils/macros.hpp" + +/** + * @brief Fully Indexable Dictionary / 完備辞書 + * @docs data_structure/fully_indexable_dictionary.md + * @note space complexity $o(N)$. $1.5N$-bit consumed + */ +class fully_indexable_dictionary { + static constexpr std::size_t block_size = 64; + std::vector block; + std::vector rank_block; // a blocked cumulative sum +public: + std::size_t size; + fully_indexable_dictionary() = default; + template + fully_indexable_dictionary(const std::vector & bits) { + size = bits.size(); + std::size_t block_count = size / block_size + 1; + block.resize(block_count); + REP (i, size) if (bits[i]) { + block[i / block_size] |= (1ull << (i % block_size)); + } + rank_block.resize(block_count); + rank_block[0] = 0; + REP (i, block_count - 1) { + rank_block[i + 1] = rank_block[i] + __builtin_popcountll(block[i]); + } + } + + /** + * @brief count the number of value in $[0, r)$ + * @note $O(1)$ + */ + int rank(bool value, int r) const { + assert (0 <= r and r <= size); + uint64_t mask = (1ull << (r % block_size)) - 1; + int rank_1 = rank_block[r / block_size] + __builtin_popcountll(block[r /block_size] & mask); + return value ? rank_1 : r - rank_1; + } + int rank(bool value, int l, int r) const { + assert (0 <= l and l <= r and r <= size); + return rank(value, r) - rank(value, l); + } + + /** + * @brief find the index of the $k$-th occurrence of value + * @note if there is no such index, returns size + * @note $O(\log N)$ + */ + int select(bool value, int k) const { + if (k >= rank(value, size)) return size; + // binary search: max { i | rank_block[i] <= k } + int l = 0, r = block.size(); // [l, r) + while (r - l > 1) { + int m = (l + r) / 2; + int rank_block_m = (value ? rank_block[m] : m * block_size - rank_block[m]); + (rank_block_m <= k ? l : r) = m; + } + int block_index = l; + // binary search: max { i | rank(i) <= k } + l = block_index * block_size; + r = std::min(size, (block_index + 1) * block_size); // [l, r) + while (r - l > 1) { + int m = (l + r) / 2; + (rank(value, m) <= k ? l : r) = m; + } + return l; + } + /** + * @brief select(value, k) in [l, size) + */ + int select(bool value, int k, int l) const { + assert (0 <= l and l <= size); + return select(value, k + rank(value, l)); + } + + /** + * @brief get the $i$-th element + * @note $O(1)$ + */ + bool access(int i) const { + assert (0 <= i and i < size); + return block[i / block_size] & (1ull << (i % block_size)); + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/fully_indexable_dictionary.md b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/fully_indexable_dictionary.md new file mode 100644 index 0000000..338e613 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/fully_indexable_dictionary.md @@ -0,0 +1,8 @@ +## 概要 + +長さ $N$ の bit 列 $b = (b_0, b_1, \dots, b _ {n - 1}) \in 2^N$ に対し、次が $O(1)$ で処理可能: + +- $\mathtt{rank}(l, r)$: 区間 $\lbrack l, r)$ 中の値 $1$ の出現回数 $$\unicode{35} \lbrace i \in \lbrack l, r) \mid a_i = 1 \rbrace$$ を計算する。 +- $\mathtt{select}(k)$: 位置 $l$ 以降で $k \ge 0$ 番目に出現する値 $1$ の位置 $i$ (つまり $i \ge l$ かつ $a_i = 1$ かつ $$\unicode{35} \lbrace j \in \lbrack l, i) \mid a_j = 1 \rbrace = k$$ を満たす $i$) を計算する。 + - ただし今回の実装では妥協の結果 $O(\log N)$ になっている +- $\mathtt{access}(i)$: 値 $b_i$ を計算する。 diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/lazy_propagation_red_black_tree.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/lazy_propagation_red_black_tree.hpp new file mode 100644 index 0000000..4f3e227 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/lazy_propagation_red_black_tree.hpp @@ -0,0 +1,365 @@ +#pragma once +#include +#include +#include +#include +#include + +/** + * @brief Lazy Propagation Segment Tree / 遅延伝播セグメント木 (monoids, 赤黒木) + * @docs data_structure/lazy_propagation_red_black_tree.md + * @tparam MonoidX is a monoid + * @tparam MonoidF is a monoid + * @tparam Action is a function phi : F * X -> X where the partial applied phi(f, -) : X -> X is a homomorphism on X + */ +template +class lazy_propagation_red_black_tree { + static_assert (std::is_invocable_r::value, ""); + typedef typename MonoidX::value_type value_type; + typedef typename MonoidF::value_type operator_type; + + enum color_t { BLACK, RED }; + struct node_t { + bool is_leaf; + value_type data; + operator_type lazy; // NOTE: this->lazy is already applied to this->data + bool reversed; + color_t color; + int rank; + int size; + node_t *left; + node_t *right; + node_t() = default; + node_t(value_type const & a_data) + : is_leaf(true) + , data(a_data) + , color(BLACK) + , rank(0) + , size(1) { + } + node_t(node_t *l, node_t *r, color_t c) // non-leaf node + : is_leaf(false) + , data(MonoidX().mult(l->data, r->data)) + , lazy(MonoidF().unit()) + , reversed(false) + , color(c) + , rank(std::max(l->rank + (l->color == BLACK), + r->rank + (r->color == BLACK))) + , size(l->size + r->size) + , left(l) + , right(r) { + } + }; + struct node_deleter { + void operator () (node_t *t) const { + assert (t != nullptr); + if (not t->is_leaf) { + (*this)(t->right); + (*this)(t->left); + } + delete t; + } + }; + + static void propagate_only_operator(node_t *a) { + MonoidF mon_f; + Action act; + if (not a->is_leaf) { + if (a->lazy != mon_f.unit()) { + auto const & l = a->left; + auto const & r = a->right; + l->data = act(a->lazy, l->data); + r->data = act(a->lazy, r->data); + if (not l->is_leaf) l->lazy = mon_f.mult(a->lazy, l->lazy); + if (not r->is_leaf) r->lazy = mon_f.mult(a->lazy, r->lazy); + a->lazy = mon_f.unit(); + } + } + } + static void propagate_only_reverse(node_t *a) { + if (not a->is_leaf) { + if (a->reversed) { + auto const & l = a->left; + auto const & r = a->right; + if (not l->is_leaf) l->reversed = not l->reversed; + if (not r->is_leaf) r->reversed = not r->reversed; + std::swap(a->left, a->right); // CAUTION: auto const & l, r are destroyed + a->reversed = false; + } + } + } + static void propagate(node_t *a) { + propagate_only_operator(a); + propagate_only_reverse(a); + } + + /** + * @note trees a, b are consumed (at set_left()/set_right()) + */ + static node_t *merge(node_t *a, node_t *b) { + if (a == nullptr) return b; + if (b == nullptr) return a; + node_t *c = merge_relax(a, b); + c->color = BLACK; + return c; + } + /* + * @note the root of returned tree may violates the color constraint + */ + static node_t *merge_relax(node_t *a, node_t *b) { + if ((a->rank) < b->rank) { + assert (not b->is_leaf); + propagate(b); + return set_left(b, merge_relax(a, b->left)); + } else if (a->rank > b->rank) { + assert (not a->is_leaf); + propagate(a); + return set_right(a, merge_relax(a->right, b)); + } else { + a->color = BLACK; + b->color = BLACK; + return new node_t(a, b, RED); + } + } + static node_t *set_left(node_t *b, node_t *c) { + if (b->color == BLACK and c->color == RED and c->left->color == RED) { + if (b->right->color == BLACK) { + *b = node_t(c->right, b->right, RED); + *c = node_t(c->left, b, BLACK); + std::swap(b, c); + } else { + b->right->color = BLACK; + c->color = BLACK; + *b = node_t(c, b->right, RED); + } + } else { + *b = node_t(c, b->right, b->color); + } + return b; + } + static node_t *set_right(node_t *a, node_t *c) { + if (a->color == BLACK and c->color == RED and c->right->color == RED) { + if (a->left->color == BLACK) { + *a = node_t(a->left, c->left, RED); + *c = node_t(a, c->right, BLACK); + std::swap(a, c); + } else { + a->left->color = BLACK; + c->color = BLACK; + *a = node_t(a->left, c, RED); + } + } else { + *a = node_t(a->left, c, a->color); + } + return a; + } + + /** + * @note tree a is consumed (at explicit delete and merge()) + */ + static std::pair split(node_t *a, int k) { + if (k == 0) { + return std::make_pair( nullptr, a ); + } + assert (a != nullptr); + if (k == a->size) { + return std::make_pair( a, nullptr ); + } + assert (not a->is_leaf); + propagate(a); + node_t *a_left = a->left; + node_t *a_right = a->right; + delete a; + if (k < a_left->size) { + node_t *l, *r; tie(l, r) = split(a_left, k); + return std::make_pair( l, merge(r, a_right) ); + } else if (k > a_left->size) { + node_t *l, *r; tie(l, r) = split(a_right, k - a_left->size); + return std::make_pair( merge(a_left, l), r ); + } else { + return std::make_pair( a_left, a_right ); + } + } + + static void range_apply(node_t *a, int l, int r, const operator_type & func) { + MonoidX mon_x; + MonoidF mon_f; + Action act; + if (l == r) return; + if (l == 0 and r == a->size) { + a->data = act(func, a->data); + if (not a->is_leaf) a->lazy = mon_f.mult(func, a->lazy); + return; + } + assert (not a->is_leaf); + propagate(a); + int k = a->left->size; + if (r <= k) { + range_apply(a->left, l, r, func); + } else if (k <= l) { + range_apply(a->right, l - k, r - k, func); + } else { + range_apply(a->left, l, k, func); + range_apply(a->right, 0, r - k, func); + } + a->data = act(a->lazy, mon_x.mult(a->left->data, a->right->data)); + } + + static value_type range_get(node_t *a, int l, int r) { + MonoidX mon_x; + assert (l < r); + if (l == 0 and r == a->size) return a->data; + assert (not a->is_leaf); + propagate(a); + int k = a->left->size; + if (r <= k) { + return range_get(a->left, l, r); + } else if (k <= l) { + return range_get(a->right, l - k, r - k); + } else { + return mon_x.mult( + range_get(a->left, l, k), + range_get(a->right, 0, r - k)); + } + } + + static node_t *reverse(node_t *a, int l, int r) { + // TODO: find ways to do without split. there may be clever ways using recursion + if (l == r) return a; + node_t *bl, *br; tie(bl, br) = split(a, r); + node_t *bm; tie(bl, bm) = split(bl, l); + if (not bm->is_leaf) bm->reversed = not bm->reversed; + return merge(merge(bl, bm), br); + } + + static void point_set(node_t *a, int i, const value_type & value) { + MonoidX mon_x; + Action act; + if (a->is_leaf) { + assert (i == 0); + a->data = value; + } else { + propagate_only_reverse(a); // should we do full propagation? + if (i < a->left->size) { + point_set(a->left, i, value); + } else { + point_set(a->right, i - a->left->size, value); + } + a->data = act(a->lazy, + mon_x.mult(a->left->data, a->right->data)); + } + } + + static value_type & point_get(node_t *a, int i) { + if (a->is_leaf) { + assert (i == 0); + return a->data; + } else { + propagate(a); + if (i < a->left->size) { + return point_get(a->left, i); + } else { + return point_get(a->right, i - a->left->size); + } + } + } + +private: + std::unique_ptr root; + +public: + lazy_propagation_red_black_tree() = default; + lazy_propagation_red_black_tree(node_t *a_root) + : root(a_root) { + } + template + lazy_propagation_red_black_tree(InputIterator first, InputIterator last) + : root(nullptr) { + for (; first != last; ++ first) { + this->push_back(*first); + } + } + + static lazy_propagation_red_black_tree merge(lazy_propagation_red_black_tree & l, lazy_propagation_red_black_tree & r) { + node_t *a = l.root.release(); + node_t *b = r.root.release(); + if (a == nullptr) return lazy_propagation_red_black_tree(b); + if (b == nullptr) return lazy_propagation_red_black_tree(a); + return lazy_propagation_red_black_tree(merge(a, b)); + } + std::pair split(int k) { + assert (0 <= k and k <= size()); + node_t *l, *r; tie(l, r) = split(root.release(), k); + return std::make_pair( lazy_propagation_red_black_tree(l), lazy_propagation_red_black_tree(r) ); + } + + void insert(int i, const value_type & data) { + assert (0 <= i and i <= size()); + if (empty()) { + root.reset(new node_t(data)); + return; + } else { + node_t *l, *r; tie(l, r) = split(root.release(), i); + root.reset( merge(merge(l, new node_t(data)), r) ); + } + } + void erase(int i) { + assert (0 <= i and i < size()); + node_t *l, *r; tie(l, r) = split(root.release(), i + 1); + node_t *m; tie(l, m) = split(l, i); + node_deleter()(m); + root.reset( merge(l, r) ); + } + + void point_set(int i, const value_type & value) { + assert (0 <= i and i < size()); + point_set(root.get(), i, value); + } + value_type const & point_get(int i) const { + assert (0 <= i and i < size()); + return point_get(const_cast(root.get()), i); + } + + void range_apply(int l, int r, const operator_type & func) { + assert (0 <= l and l <= r and r <= size()); + if (l == r) return; + range_apply(root.get(), l, r, func); + } + value_type const range_get(int l, int r) const { + assert (0 <= l and l <= r and r <= size()); + if (l == r) return MonoidX().unit(); + return range_get(const_cast(root.get()), l, r); + } + void reverse(int l, int r) { + assert (0 <= l and l <= r and r <= size()); + if (not root) return; + root.reset( reverse(root.release(), l, r) ); + } + + void push_back(const value_type & data) { + root.reset( merge(root.release(), new node_t(data)) ); + } + void push_front(const value_type & data) { + root.reset( merge(new node_t(data), root.release()) ); + } + void pop_back() { + int k = size() - 1; + auto lr = split(root.release(), k); + root.reset(lr.first); + node_deleter()(lr.second); + } + void pop_front() { + auto lr = split(root.release(), 1); + node_deleter()(lr.first); + root.reset(lr.second); + } + int size() const { + return root ? root.get()->size : 0; + } + bool empty() const { + return not root; + } + void clear() { + root = nullptr; + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/lazy_propagation_red_black_tree.md b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/lazy_propagation_red_black_tree.md new file mode 100644 index 0000000..eb808e5 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/lazy_propagation_red_black_tree.md @@ -0,0 +1,7 @@ +## Operations + +完全二分木上の遅延伝播セグメント木でできることに加え、$O(\log N)$ で次ができる: + +- $\mathtt{insert}(i, b)$: 列の $i$ 番目の要素の前に要素 $b$ を挿入する。 +- $\mathtt{erase}(i)$: 列の $i$ 番目の要素を削除する。 +- $\mathtt{reverse}(l, r)$: 列の $l, l+1, \dots, r-1$ 番目の要素を反転する。つまり $(a_0, a_1, \dots, a _ {l-1}, a_l, a _ {l+1}, \dots, a _ {r-1}, a_r, a _ {r + 1}, \dots, a _ {N - 1}) \gets (a_0, a_1, \dots, a _ {l-1}, a _ {r-1}, a _ {r-2}, \dots, a_l, a_r, a _ {r + 1}, \dots, a _ {N - 1})$ と更新する。 diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/lazy_propagation_red_black_tree.range_affine_range_sum.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/lazy_propagation_red_black_tree.range_affine_range_sum.test.cpp new file mode 100644 index 0000000..090b493 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/lazy_propagation_red_black_tree.range_affine_range_sum.test.cpp @@ -0,0 +1,37 @@ +#define PROBLEM "https://judge.yosupo.jp/problem/range_affine_range_sum" +#include "../data_structure/lazy_propagation_red_black_tree.hpp" +#include "../monoids/plus_count.hpp" +#include "../monoids/linear_function.hpp" +#include "../monoids/linear_function_plus_count_action.hpp" +#include "../modulus/mint.hpp" +#include "../utils/macros.hpp" +#include +#include +#include +using namespace std; + +constexpr int MOD = 998244353; + +int main() { + int n, q; scanf("%d%d", &n, &q); + vector, int> > a(n); + REP (i, n) { + int a_i; scanf("%d", &a_i); + a[i].first = a_i; + a[i].second = 1; + } + lazy_propagation_red_black_tree >, linear_function_monoid >, linear_function_plus_count_action > > segtree(ALL(a)); + while (q --) { + int t; scanf("%d", &t); + if (t == 0) { + int l, r, b, c; scanf("%d%d%d%d", &l, &r, &b, &c); + pair, mint > f(b, c); + segtree.range_apply(l, r, f); + } else if (t == 1) { + int l, r; scanf("%d%d", &l, &r); + mint answer = segtree.range_get(l, r).first; + printf("%d\n", answer.value); + } + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/lazy_propagation_segment_tree.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/lazy_propagation_segment_tree.hpp new file mode 100644 index 0000000..043265e --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/lazy_propagation_segment_tree.hpp @@ -0,0 +1,112 @@ +#pragma once +#include +#include +#include +#include +#include "../utils/macros.hpp" + +/** + * @brief Lazy Propagation Segment Tree / 遅延伝播セグメント木 (monoids, 完全二分木) + * @docs data_structure/lazy_propagation_segment_tree.md + * @tparam MonoidX is a monoid + * @tparam MonoidF is a monoid + * @tparam Action is a function phi : F * X -> X where the partial applied phi(f, -) : X -> X is a homomorphism on X + */ +template +struct lazy_propagation_segment_tree { + static_assert (std::is_invocable_r::value, ""); + typedef typename MonoidX::value_type value_type; + typedef typename MonoidF::value_type operator_type; + MonoidX mon_x; + MonoidF mon_f; + Action act; + int n; + std::vector a; + std::vector f; + + lazy_propagation_segment_tree() = default; + lazy_propagation_segment_tree(int n_, const MonoidX & mon_x_ = MonoidX(), const MonoidF & mon_f_ = MonoidF(), const Action & act_ = Action()) + : mon_x(mon_x_), mon_f(mon_f_), act(act_) { + n = 1; while (n < n_) n *= 2; + a.resize(2 * n - 1, mon_x.unit()); + f.resize(n - 1, mon_f.unit()); + } + template + lazy_propagation_segment_tree(InputIterator first, InputIterator last, const MonoidX & mon_x_ = MonoidX(), const MonoidF & mon_f_ = MonoidF(), const Action & act_ = Action()) + : mon_x(mon_x_), mon_f(mon_f_), act(act_) { + int size = std::distance(first, last); + n = 1; while (n < size) n *= 2; + a.resize(2 * n - 1, mon_x.unit()); + f.resize(n - 1, mon_f.unit()); + std::copy(first, last, a.begin() + (n - 1)); + REP_R (i, n - 1) { + a[i] = mon_x.mult(a[2 * i + 1], a[2 * i + 2]); + } + } + + void point_set(int i, value_type b) { + range_set(i, i + 1, b); + } + /** + * @note O(min(n, (r - l) log n)) + */ + void range_set(int l, int r, value_type b) { + assert (0 <= l and l <= r and r <= n); + range_set(0, 0, n, l, r, b); + } + void range_set(int i, int il, int ir, int l, int r, value_type b) { + if (l <= il and ir <= r and ir - il == 1) { // 0-based + a[i] = b; + } else if (ir <= l or r <= il) { + // nop + } else { + range_apply(2 * i + 1, il, (il + ir) / 2, 0, n, f[i]); + range_apply(2 * i + 2, (il + ir) / 2, ir, 0, n, f[i]); + f[i] = mon_f.unit(); + range_set(2 * i + 1, il, (il + ir) / 2, l, r, b); + range_set(2 * i + 2, (il + ir) / 2, ir, l, r, b); + a[i] = mon_x.mult(a[2 * i + 1], a[2 * i + 2]); + } + } + + void point_apply(int i, operator_type g) { + range_apply(i, i + 1, g); + } + void range_apply(int l, int r, operator_type g) { + assert (0 <= l and l <= r and r <= n); + range_apply(0, 0, n, l, r, g); + } + void range_apply(int i, int il, int ir, int l, int r, operator_type g) { + if (l <= il and ir <= r) { // 0-based + a[i] = act(g, a[i]); + if (i < f.size()) f[i] = mon_f.mult(g, f[i]); + } else if (ir <= l or r <= il) { + // nop + } else { + range_apply(2 * i + 1, il, (il + ir) / 2, 0, n, f[i]); + range_apply(2 * i + 2, (il + ir) / 2, ir, 0, n, f[i]); + f[i] = mon_f.unit(); // unnecessary if the oprator monoid is commutative + range_apply(2 * i + 1, il, (il + ir) / 2, l, r, g); + range_apply(2 * i + 2, (il + ir) / 2, ir, l, r, g); + a[i] = mon_x.mult(a[2 * i + 1], a[2 * i + 2]); + } + } + + value_type point_get(int i) { + return range_get(i, i + 1); + } + value_type range_get(int l, int r) { + assert (0 <= l and l <= r and r <= n); + if (l == 0 and r == n) return a[0]; + value_type lacc = mon_x.unit(), racc = mon_x.unit(); + for (int l1 = (l += n), r1 = (r += n) - 1; l1 > 1; l /= 2, r /= 2, l1 /= 2, r1 /= 2) { // 1-based loop, 2x faster than recursion + if (l < r) { + if (l % 2 == 1) lacc = mon_x.mult(lacc, a[(l ++) - 1]); + if (r % 2 == 1) racc = mon_x.mult(a[(-- r) - 1], racc); + } + lacc = act(f[l1 / 2 - 1], lacc); + racc = act(f[r1 / 2 - 1], racc); + } + return mon_x.mult(lacc, racc); + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/lazy_propagation_segment_tree.md b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/lazy_propagation_segment_tree.md new file mode 100644 index 0000000..5651feb --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/lazy_propagation_segment_tree.md @@ -0,0 +1,21 @@ +## 概要 + +次があるとする: + +- monoid $M = (M, \cdot, 1)$ +- monoid $F = (F, \circ, \mathrm{id})$ +- monoid $F$ の半群 $M$ に対する作用 $\star$。つまり関数 $\star : F \times M \to M$ であって、次を満たすもの + - $\forall a \in M.~ \mathrm{id} \star a = a$ + - $\forall f, g \in F.~ \forall a \in M.~ (f \circ g) \star a = f \star (g \star a)$ + - $\forall f \in F.~ \forall a, b \in M.~ f \star (a \cdot b) = (f \star a) \cdot (f \star b)$ + +このとき monoid $M$ の要素の列 $a = (a_0, a_1, \dots, a _ {n - 1}) \in M^n$ に対し、次が $O(\log N)$ で処理可能: + +- $\mathtt{range\unicode{95}apply}(l, r, g)$: それぞれの $i \in [l, r)$ に対し $a_i \gets g \star a_i$ と更新する。 +- $\mathtt{range\unicode{95}get}(l, r)$: 積 $a_l \cdot a _ {l + 1} \cdot \dots \cdot a _ {r - 1}$ を計算する。 + +他にも: + +- $\mathtt{point\unicode{95}get}(i)$: 値 $a_i$ を計算する。 +- $\mathtt{point\unicode{95}set}(i, b)$: $a_i \gets b$ と更新する。 +- $\mathtt{range\unicode{95}set}(l, r, b)$: それぞれの $i \in [l, r)$ に対し $a_i \gets b$ と更新する。 diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/lazy_propagation_segment_tree.range_affine_range_sum.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/lazy_propagation_segment_tree.range_affine_range_sum.test.cpp new file mode 100644 index 0000000..1c2e6b0 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/lazy_propagation_segment_tree.range_affine_range_sum.test.cpp @@ -0,0 +1,37 @@ +#define PROBLEM "https://judge.yosupo.jp/problem/range_affine_range_sum" +#include "../data_structure/lazy_propagation_segment_tree.hpp" +#include "../monoids/plus_count.hpp" +#include "../monoids/linear_function.hpp" +#include "../monoids/linear_function_plus_count_action.hpp" +#include "../modulus/mint.hpp" +#include "../utils/macros.hpp" +#include +#include +#include +using namespace std; + +constexpr int MOD = 998244353; + +int main() { + int n, q; scanf("%d%d", &n, &q); + vector, int> > a(n); + REP (i, n) { + int a_i; scanf("%d", &a_i); + a[i].first = a_i; + a[i].second = 1; + } + lazy_propagation_segment_tree >, linear_function_monoid >, linear_function_plus_count_action > > segtree(ALL(a)); + while (q --) { + int t; scanf("%d", &t); + if (t == 0) { + int l, r, b, c; scanf("%d%d%d%d", &l, &r, &b, &c); + pair, mint > f(b, c); + segtree.range_apply(l, r, f); + } else if (t == 1) { + int l, r; scanf("%d%d", &l, &r); + mint answer = segtree.range_get(l, r).first; + printf("%d\n", answer.value); + } + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/lazy_propagation_segment_tree.range_min_range_add.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/lazy_propagation_segment_tree.range_min_range_add.test.cpp new file mode 100644 index 0000000..ca00ef9 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/lazy_propagation_segment_tree.range_min_range_add.test.cpp @@ -0,0 +1,23 @@ +#define PROBLEM "http://judge.u-aizu.ac.jp/onlinejudge/description.jsp?id=DSL_2_H" +#include "../data_structure/lazy_propagation_segment_tree.hpp" +#include "../monoids/min.hpp" +#include "../monoids/plus.hpp" +#include "../monoids/plus_min_action.hpp" +#include + +int main() { + int n, q; scanf("%d%d", &n, &q); + lazy_propagation_segment_tree, plus_monoid, plus_min_action > segtree(n); + segtree.range_set(0, n, 0); + while (q --) { + int com, l, r; scanf("%d%d%d", &com, &l, &r); + ++ r; + if (com == 0) { + int x; scanf("%d", &x); + segtree.range_apply(l, r, x); + } else if (com == 1) { + printf("%d\n", segtree.range_get(l, r)); + } + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/li_chao_tree.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/li_chao_tree.hpp new file mode 100644 index 0000000..697f2d6 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/li_chao_tree.hpp @@ -0,0 +1,102 @@ +#pragma once +#include +#include +#include +#include +#include +#include "../utils/macros.hpp" + +/** + * @brief Li-Chao tree + * @docs data_structure/li_chao_tree.md + */ +struct li_chao_tree { + int k; + std::vector > f; + std::vector x; + std::vector y; + li_chao_tree() = default; + li_chao_tree(const std::vector & x_) : x(x_) { + assert (std::is_sorted(ALL(x))); + k = 0; while ((1 << k) < x.size()) ++ k; + f.resize((1 << k) - 1, std::make_pair(0, std::numeric_limits::max())); + y.resize(x.size(), std::numeric_limits::max()); + } + /** + * @note i is a point on the original space + */ + int64_t get_min(int64_t i) { + i = std::lower_bound(ALL(x), i) - x.begin(); + return get_min_compressed(i); + } + int64_t get_min_compressed(int i) { // 0-based + assert (0 <= i and i < x.size()); + int64_t z = y[i]; + for (int j = (i + (1 << k)) / 2; j > 0; j /= 2) { // 1-based + z = std::min(z, f[j - 1].first * x[i] + f[j - 1].second); + } + return z; + } + /** + * @note [l, r) is an interval on the original space + */ + void add_segment(int64_t l, int64_t r, int64_t a, int64_t b) { + l = std::lower_bound(ALL(x), l) - x.begin(); + r = std::lower_bound(ALL(x), r) - x.begin(); + add_segment_compressed(l, r, a, b); + } + void add_segment_compressed(int64_t l, int64_t r, int64_t a, int64_t b) { + assert (0 <= l and l <= r and r <= x.size()); + add_segment_compressed(0, 0, 1 << k, l, r, a, b); + } + void add_segment_compressed(int i, int il, int ir, int l, int r, int64_t a, int64_t b) { + if (l <= il and ir <= r) { // 0-based + if (i >= (1 << k) - 1) { + int j = i - (1 << k) + 1; + y[j] = std::min(y[j], a * x[j] + b); + } else { + int64_t xl = x[il]; + int64_t xr = x[ir - 1]; // [xl, xr] + int64_t yl1 = f[i].first * xl + f[i].second; + int64_t yr1 = f[i].first * xr + f[i].second; + int64_t yl2 = a * xl + b; + int64_t yr2 = a * xr + b; + if (yl1 <= yl2 and yr1 <= yr2) { + // if forall x in [l, r]. f(x) <= g(x), then do nothing + // nop + } else if (yl2 <= yl1 and yr2 <= yr1) { + // if forall x in [l, r]. g(x) <= f(x), then replace f with g + f[i] = std::make_pair(a, b); + } else { + int64_t xm = x[(il + ir) / 2]; + int64_t ym1 = f[i].first * xm + f[i].second; + int64_t ym2 = a * xm + b; + if (yl1 <= yl2 and ym1 <= ym2) { + // if forall x in [l, m]. f(x) <= g(x), then do recursion for [m, r] with f + add_segment_compressed(2 * i + 2, (il + ir) / 2, ir, l, r, a, b); + } else if (yl2 <= yl1 and ym2 <= ym1) { + // if forall x in [l, m]. g(x) <= f(x), then replace f with g and do recursion for [m, r] with g + std::swap(a, f[i].first); + std::swap(b, f[i].second); + add_segment_compressed(2 * i + 2, (il + ir) / 2, ir, l, r, a, b); + } else if (ym1 <= ym2 and yr1 <= yr2) { + // if forall x in [m, r]. f(x) <= g(x), then do recursion for [l, m] with f + add_segment_compressed(2 * i + 1, il, (il + ir) / 2, l, r, a, b); + } else if (ym2 <= ym1 and yr2 <= yr1) { + // if forall x in [m, r]. g(x) <= f(x), then replace f with g and do recursion for [l, m] with g + std::swap(a, f[i].first); + std::swap(b, f[i].second); + add_segment_compressed(2 * i + 1, il, (il + ir) / 2, l, r, a, b); + } else { + assert (false); + } + } + } + } else if (ir <= l or r <= il) { + // nop + } else { + add_segment_compressed(2 * i + 1, il, (il + ir) / 2, l, r, a, b); + add_segment_compressed(2 * i + 2, (il + ir) / 2, ir, l, r, a, b); + } + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/li_chao_tree.md b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/li_chao_tree.md new file mode 100644 index 0000000..b00eb5c --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/li_chao_tree.md @@ -0,0 +1,7 @@ +## 概要 + +ありえる座標の有限集合 $X \subseteq \mathbb{Z}$ が固定されているとする。 +一次関数と区間の対の集合 $F = \emptyset \subseteq \lbrace \lbrack l, r) \mid l, r \in X \wedge l \le r \rbrace \times \lbrace \lambda x. ax + b \mid a, b \in \mathbb{Z} \rbrace$ に対し、次が $O(\log N)$ amortized (一部は $O(\log N)$) で処理可能: + +- $\mathtt{add\unicode{95}segment}(l, r, f)$: $l, r \in X$ でなければならない。$F \gets F \cup \lbrace (f, \lbrack l, r) \rbrace$ と更新する。 +- $\mathtt{get\unicode{95}min}(x)$: $x \in X$ でなければならない。$$\min _ {(f, \lbrack l, r) \in F \wedge x \lbrack l, r))} f(x)$$ を $O(\log N)$ で計算する。 diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/li_chao_tree.segment_add_get_min.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/li_chao_tree.segment_add_get_min.test.cpp new file mode 100644 index 0000000..e59b624 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/li_chao_tree.segment_add_get_min.test.cpp @@ -0,0 +1,62 @@ +#define PROBLEM "https://judge.yosupo.jp/problem/segment_add_get_min" +#include "../data_structure/li_chao_tree.hpp" +#include "../utils/macros.hpp" +#include +#include +#include +#include +using namespace std; + +int main() { + // input + int n, q; scanf("%d%d", &n, &q); + vector xs; + vector > segments; + REP (i, n) { + int64_t l, r, a, b; scanf("%lld%lld%lld%lld", &l, &r, &a, &b); + xs.push_back(l); + xs.push_back(r); + segments.emplace_back(l, r, a, b); + } + vector > queries; + REP (j, q) { + int t; scanf("%d", &t); + if (t == 0) { + int64_t l, r, a, b; scanf("%lld%lld%lld%lld", &l, &r, &a, &b); + xs.push_back(l); + xs.push_back(r); + queries.emplace_back(t, l, r, a, b, -1); + } else if (t == 1) { + int64_t p; scanf("%lld", &p); + xs.push_back(p); + queries.emplace_back(t, -1, -1, -1, -1, p); + } else { + assert (false); + } + } + + // construct + sort(ALL(xs)); + xs.erase(unique(ALL(xs)), xs.end()); + li_chao_tree lichao(xs); + + // output + for (auto [l, r, a, b] : segments) { + lichao.add_segment(l, r, a, b); + } + for (auto [t, l, r, a, b, p] : queries) { + if (t == 0) { + lichao.add_segment(l, r, a, b); + } else if (t == 1) { + int64_t q = lichao.get_min(p); + if (q == INT64_MAX) { + printf("INFINITY\n"); + } else { + printf("%lld\n", q); + } + } else { + assert (false); + } + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/link_cut_tree.dynamic_tree_reduce_loop.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/link_cut_tree.dynamic_tree_reduce_loop.test.cpp new file mode 100644 index 0000000..17b2978 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/link_cut_tree.dynamic_tree_reduce_loop.test.cpp @@ -0,0 +1,40 @@ +//#define PROBLEM "https://judge.yosupo.jp/problem/dynamic_tree_vertex_add_path_sum" +#include "../data_structure/link_cut_tree.hpp" +#include "../monoids/plus.hpp" +#include "../utils/macros.hpp" +#include "../hack/fastio.hpp" +#include +#include +#include +using namespace std; +void print_graph(link_cut_tree > lct, int n){ + for(int i=0; i > lct(n); + lct.vertex_set(1,1); + lct.vertex_set(0,0); + lct.vertex_set(2,2); + lct.vertex_set(3,3); + lct.vertex_set(4,4); + lct.link(1,0); + lct.link(2,0); + lct.link(3,2); + lct.link(4,2); + print_graph(lct, n); + + lct.cut(2); + lct.link(2,1); + print_graph(lct, n); + + //std::cout< +#include +using namespace std; + +int main() { + int n = in(); + int q = in(); + + // initialize + link_cut_tree > lct(n); + REP (i, n) { + int64_t a_i = in(); + lct.vertex_set(i, a_i); + } + vector > g(n); + REP (i, n - 1) { + int u = in(); + int v = in(); + g[u].push_back(v); + g[v].push_back(u); + } + vector used(n); + stack stk; + stk.push(0); + used[0] = true; + while (not stk.empty()) { + int u = stk.top(); + stk.pop(); + for (int v : g[u]) if (not used[v]) { + lct.link(v, u); + stk.push(v); + used[v] = true; + } + } + + // answer to queries + while (q --) { + int t = in(); + if (t == 0) { + int u = in(); + int v = in(); + int w = in(); + int x = in(); + lct.cut(lct.get_parent(u) == v ? u : v); + lct.evert(w); + lct.link(w, x); + } else if (t == 1) { + int p = in(); + int64_t x = in(); + lct.vertex_set(p, lct.vertex_get(p) + x); + } else if (t == 2) { + int u = in(); + int v = in(); + out(lct.path_get(u, v)); + out('\n'); + } + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/link_cut_tree.dynamic_tree_vertex_set_path_composite.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/link_cut_tree.dynamic_tree_vertex_set_path_composite.test.cpp new file mode 100644 index 0000000..5130ee4 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/link_cut_tree.dynamic_tree_vertex_set_path_composite.test.cpp @@ -0,0 +1,70 @@ +#define PROBLEM "https://judge.yosupo.jp/problem/dynamic_tree_vertex_set_path_composite" +#include "../data_structure/link_cut_tree.hpp" +#include "../modulus/mint.hpp" +#include "../monoids/linear_function.hpp" +#include "../monoids/dual.hpp" +#include "../utils/macros.hpp" +#include "../hack/fastio.hpp" +#include +#include + +constexpr int MOD = 998244353; +int main() { + int n = in(); + int q = in(); + + // initialize + link_cut_tree > > > lct(n); + REP (i, n) { + mint a_i = in(); + mint b_i = in(); + lct.vertex_set(i, std::make_pair(a_i, b_i)); + } + std::vector > g(n); + REP (i, n - 1) { + int u = in(); + int v = in(); + g[u].push_back(v); + g[v].push_back(u); + } + std::vector used(n); + std::stack stk; + stk.push(0); + used[0] = true; + while (not stk.empty()) { + int u = stk.top(); + stk.pop(); + for (int v : g[u]) if (not used[v]) { + lct.link(v, u); + stk.push(v); + used[v] = true; + } + } + + // answer to queries + while (q --) { + int t = in(); + if (t == 0) { + int u = in(); + int v = in(); + int w = in(); + int x = in(); + lct.cut(lct.get_parent(u) == v ? u : v); + lct.evert(w); + lct.link(w, x); + } else if (t == 1) { + int u = in(); + mint c = in(); + mint d = in(); + lct.vertex_set(u, std::make_pair(c, d)); + } else if (t == 2) { + int u = in(); + int v = in(); + mint x = in(); + auto [a, b] = lct.path_get(u, v); + out((a * x + b).value); + out('\n'); + } + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/link_cut_tree.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/link_cut_tree.hpp new file mode 100644 index 0000000..6703b8e --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/link_cut_tree.hpp @@ -0,0 +1,330 @@ +#pragma once +#include +#include +#include +#include +#include +#include "../utils/macros.hpp" +#include "../monoids/reversible.hpp" + + +/** + * @brief Link-Cut tree (monoids without commutativity, vertex set + path get) + * @docs data_structure/link_cut_tree.md + * @description manages a dynamic forest of rooted trees + * @note in each splay tree, nodes are sorted from bottom to top. the rightmost node of the root splay tree of the auxiliary tree is the root of represented tree. + */ +template +class link_cut_tree { + typedef typename Monoid::value_type value_type; + typedef typename reversible_monoid::value_type reversible_type; + const reversible_monoid mon; + std::vector data; // data of the original tree + std::vector path; // sum of data of the sub-tree in the belonging splay tree + std::vector parent, left, right; // of the auxiliary tree + std::vector is_reversed; // of the auxiliary tree; not applied to left, right, and path yet + + /** + * @description returns whether `a` and `parent[a]` is in the same splay tree + */ + int get_parent_edge_type(int a) const { + if (parent[a] != -1 and left[parent[a]] == a) { + assert (not is_reversed[parent[a]]); + return -1; // heavy (left-child) + } else if (parent[a] != -1 and right[parent[a]] == a) { + assert (not is_reversed[parent[a]]); + return +1; // heavy (right-child) + } else { + return 0; // light + } + } + + /** + * @description make `a` the parent of the current parent of `a` + */ + void rotate(int a) { + int b = parent[a]; + assert (b != -1); + assert (not is_reversed[b]); + assert (not is_reversed[a]); + switch (get_parent_edge_type(b)) { + case -1: // left + left[parent[b]] = a; + break; + case 1: // right + right[parent[b]] = a; + break; + } + switch (get_parent_edge_type(a)) { + case -1: // left + parent[a] = std::exchange(parent[b], a); + left[b] = std::exchange(right[a], b); + if (left[b] != -1) parent[left[b]] = b; + break; + case 1: // right + parent[a] = std::exchange(parent[b], a); + right[b] = std::exchange(left[a], b); + if (right[b] != -1) parent[right[b]] = b; + break; + default: // root + assert (false); + } + update_path(b); + update_path(a); + } + + /** + * @description make `a` the root of the beloging splay tree + */ + void splay(int a) { + propagate_reverse_splay(a); + while (get_parent_edge_type(a)) { + // a is not the root of the splay tree + if (not get_parent_edge_type(parent[a])) { + // zig step: parent[a] is the root of the splay tree + rotate(a); + } else if (get_parent_edge_type(a) == get_parent_edge_type(parent[a])) { + // zig-zig step + rotate(parent[a]); + rotate(a); + } else { + // zig-zag step + rotate(a); + rotate(a); + } + } + } + + /** + * @description make `a` the root of the auxiliary tree + * @note `a` becomes a terminal of the heavy path + */ + void expose(int a) { + // make a light path from `a` to the root + for (int b = a; b != -1; b = parent[b]) { + splay(b); + } + // make `a` the terminal of the path + assert (not is_reversed[a]); + left[a] = -1; + update_path(a); + // make the path heavy + for (int b = a; parent[b] != -1; b = parent[b]) { + left[parent[b]] = b; + update_path(parent[b]); + } + splay(a); + } + + void propagate_reverse_node(int a) { + if (is_reversed[a]) { + is_reversed[a] = false; + path[a] = reversible_monoid::reverse(path[a]); + if (right[a] != -1) is_reversed[right[a]] = not is_reversed[right[a]]; + if (left[a] != -1) is_reversed[left[a]] = not is_reversed[left[a]]; + std::swap(left[a], right[a]); + } + } + void propagate_reverse_splay(int a) { + if (parent[a] != -1 and (left[parent[a]] == a or right[parent[a]] == a)) { + propagate_reverse_splay(parent[a]); + } + propagate_reverse_node(a); + if (right[a] != -1) propagate_reverse_node(right[a]); + if (left[a] != -1) propagate_reverse_node(left[a]); + } + + /** + * @note `a` should be the root of the splay tree + */ + void update_path(int a) { + path[a] = reversible_monoid::make(data[a]); + if (right[a] != -1) path[a] = mon.mult(path[a], path[right[a]]); + if (left[a] != -1) path[a] = mon.mult(path[left[a]], path[a]); + } + +public: + link_cut_tree(int size, const Monoid & mon_ = Monoid()) + : mon(mon_), data(size, mon.base.unit()), path(size, mon.unit()), parent(size, -1), left(size, -1), right(size, -1), is_reversed(size, false) { + } + + /** + * @description make a direct edge from `a` to `b` + * @note `a` must be a root + * @note `b` must not be a descendant of `a` + */ + void link(int a, int b) { + expose(b); // for the time complexity + expose(a); // to make `a` the root + assert (not is_reversed[a]); + assert (right[a] == -1); // `a` must be a root + parent[a] = b; + } + + /** + * @description remove the direct edge from `a` + * @note `a` must not be a root + */ + void cut(int a) { + expose(a); // to make `a` the root + assert (not is_reversed[a]); + assert (right[a] != -1); // `a` must not be a root + parent[right[a]] = -1; + right[a] = -1; + update_path(a); + } + + /** + * @note -1 is returned when `a` and `b` are not in the same tree + */ + int get_lowest_common_ancestor(int a, int b) { + expose(b); // for the time complexity + expose(a); // to make `a` the root + assert (not is_reversed[a]); + int preserved = -1; + std::swap(left[a], preserved); // make `a` and `b` belong different splay trees even if `b` is a descendant of `a` + int result = b; + int c = b; + for (; c != a and c != -1; c = parent[c]) { + assert (parent[c] != -1); + if (not get_parent_edge_type(c)) { // when it enters another splay tree + result = parent[c]; + } + } + std::swap(left[a], preserved); + return c == a ? result : -1; + } + + bool are_connected(int a, int b) { + return get_lowest_common_ancestor(a, b) != -1; + } + + int get_parent(int a) { + expose(a); + assert (not is_reversed[a]); + if (right[a] == -1) return parent[a]; + for (int b = right[a]; ; b = left[b]) { + propagate_reverse_node(b); + if (left[b] == -1) { + splay(b); // for the time complexity + return b; + } + } + } + + int get_root(int a) { + expose(a); + assert (not is_reversed[a]); + while (right[a] != -1) { + a = right[a]; + } + splay(a); // for the time complexity + return a; + } + + /** + * @description make `a` the root of the represented tree + */ + void evert(int a) { + expose(a); // to make `a` the root + assert (not is_reversed[a]); + assert (left[a] == -1); // `a` is the terminal + is_reversed[a] = true; + } + /** + * DOENST WORK!!! left and right can both be -1 even if they aren't... + * @description make `a` not the root of the represented tree by making one of its children the root + */ + void avoid_root(int a) { + std::cout<<"avoid_root inside: "<::make(data[a]); + reversible_type up = (right[a] == -1 ? data_a : mon.mult(data_a, path[right[a]])); + reversible_type down = (left[a] == -1 ? data_a : mon.mult(path[left[a]], data_a)); + for (int c = a; c != b and c != -1; c = parent[c]) { + assert (parent[c] != -1); + assert (not is_reversed[parent[c]]); + auto data_parent_c = reversible_monoid::make(data[parent[c]]); + switch (get_parent_edge_type(c)) { + case -1: // heavy (left-child) + up = mon.mult(up, data_parent_c); + if (right[parent[c]] != -1) up = mon.mult(up, path[right[parent[c]]]); + break; + case 1: // heavy (right-child) + down = mon.mult(data_parent_c, down); + if (left[parent[c]] != -1) down = mon.mult(path[left[parent[c]]], down); + break; + case 0: // light + down = reversible_monoid::reverse(up); + up = mon.mult(up, data_parent_c); + if (right[parent[c]] != -1) up = mon.mult(up, path[right[parent[c]]]); + down = mon.mult(data_parent_c, down); + if (left[parent[c]] != -1) down = mon.mult(path[left[parent[c]]], down); + break; + } + } + return reversible_monoid::get(reversible_monoid::reverse(down)); + } + + std::string to_graphviz() const { + using namespace std; + ostringstream oss; + oss << "digraph G {" << endl; + oss << " graph [ rankdir = BT, bgcolor = \"#00000000\" ]" << endl; + oss << " node [ shape = circle, style = filled, fillcolor = \"#ffffffff\" ]" << endl; + REP (a, parent.size()) { + // oss << " " << a << ";" << endl; + oss << " " << a << "[ label = \"" << a << "(" << data[a] << "," << path[a] << (is_reversed[a] ? " +rev" : "") << ")\"];" << endl; + } + function go = [&](int a) { + if (parent[a] != -1 and not get_parent_edge_type(a)) { + oss << " " << a << " -> " << parent[a] << " [ style = dashed ]" << endl; + } + if (left[a] != -1 or right[a] != -1) { + string l = (left[a] != -1 ? to_string(left[a]) : "l" + to_string(a)); + string r = (right[a] != -1 ? to_string(right[a]) : "r" + to_string(a)); + oss << " " << l << " -> " << a << endl; + oss << " " << l << " -> " << r << " [ style = invis ]" << endl; + oss << " " << r << " -> " << a << endl; + oss << " { rank = same; " << l << "; " << r << "; }" << endl; + if (left[a] == -1) oss << " " << l << " [ style = invis ]" << endl; + if (right[a] == -1) oss << " " << r << " [ style = invis ]" << endl; + } + if (left[a] != -1) go(left[a]); + if (right[a] != -1) go(right[a]); + }; + REP (a, parent.size()) if (not get_parent_edge_type(a)) { + go(a); + } + oss << "}"; + return oss.str(); + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/link_cut_tree.marked_ancestor.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/link_cut_tree.marked_ancestor.test.cpp new file mode 100644 index 0000000..c6651f6 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/link_cut_tree.marked_ancestor.test.cpp @@ -0,0 +1,39 @@ +#define PROBLEM "http://judge.u-aizu.ac.jp/onlinejudge/description.jsp?id=2170&lang=jp" +#include +#include "../data_structure/link_cut_tree.hpp" +#include "../monoids/trivial.hpp" +#include "../utils/macros.hpp" +using namespace std; + + +int main() { + while (true) { + int n, q; cin >> n >> q; + if (n == 0 and q == 0) break; + + link_cut_tree lct(n); + REP3 (i, 1, n) { + int parent; cin >> parent; + -- parent; + lct.link(i, parent); + } + + long long sum = 0; + vector marked(n); + marked[0] = true; + while (q --) { + char c; int v; cin >> c >> v; + -- v; + if (c == 'M') { + if (not marked[v]) { + marked[v] = true; + lct.cut(v); + } + } else if (c == 'Q') { + sum += lct.get_root(v) + 1; + } + } + cout << sum << endl; + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/link_cut_tree.md b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/link_cut_tree.md new file mode 100644 index 0000000..aa3da62 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/link_cut_tree.md @@ -0,0 +1,18 @@ +outlook## Operations + +根付き木からなる森 $G$ を動的に操作できる。頂点数を $N$ として均し $O(\log N)$ で次が処理できる。 + +- $\mathtt{link}(x, y)$: 根である頂点 $x$ から頂点 $y$ に辺を追加する。 +- $\mathtt{cut}(x)$: 根でない頂点 $x$ とその親との間の辺を削除する。 +- この森における接続性についての様々な質問 + - $\mathtt{get\unicode{95}parent}(x)$ + - $\mathtt{get\unicode{95}root}(x)$ + - $\mathtt{are\unicode{95}connected}(x, y)$ + - $\mathtt{get\unicode{95}lowest\unicode{95}common\unicode{95}ancestor}(x, y)$ +- $\mathtt{evert}(x)$: 頂点 $x$ を根にする。つまり、頂点 $x$ からその所属する木の根までの辺をすべて反転させる。 + +加えて、それぞれの頂点 $x$ に monoid 値重み $a_x \in M$ を乗せて、次が処理できる。 + +- $\mathtt{vertex\unicode{95}set}(x, b)$: 頂点 $x$ の重みを $a_x \gets b$ と更新する。 +- $\mathtt{vertex\unicode{95}get}(x, b)$: 頂点 $x$ の重み $a_x \gets b$ を計算する。 +- $\mathtt{path\unicode{95}get}(x, y)$: 頂点 $x$ から $y$ への唯一のパス $x, z_1, z_2, \dots, y$ に沿った重みの積 $a_x \cdot a _ {z_1} \cdot a _ {z_2} \cdot \dots \cdot a_y$ を計算する。 diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/link_cut_tree.vertex_add_path_sum.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/link_cut_tree.vertex_add_path_sum.test.cpp new file mode 100644 index 0000000..4d0c80a --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/link_cut_tree.vertex_add_path_sum.test.cpp @@ -0,0 +1,56 @@ +#define PROBLEM "https://judge.yosupo.jp/problem/vertex_add_path_sum" +#include "../data_structure/link_cut_tree.hpp" +#include "../monoids/plus.hpp" +#include "../utils/macros.hpp" +#include "../hack/fastio.hpp" +#include +#include +using namespace std; + +int main() { + int n = in(); + int q = in(); + + // initialize + link_cut_tree > lct(n); + REP (i, n) { + int64_t a_i = in(); + lct.vertex_set(i, a_i); + } + vector > g(n); + REP (i, n - 1) { + int u = in(); + int v = in(); + g[u].push_back(v); + g[v].push_back(u); + } + vector used(n); + stack stk; + stk.push(0); + used[0] = true; + while (not stk.empty()) { + int u = stk.top(); + stk.pop(); + for (int v : g[u]) if (not used[v]) { + lct.link(v, u); + stk.push(v); + used[v] = true; + } + } + + // answer to queries + while (q --) { + int t = in(); + if (t == 0) { + int p = in(); + int64_t x = in(); + lct.vertex_set(p, lct.vertex_get(p) + x); + } else if (t == 1) { + int u = in(); + int v = in(); + out(lct.path_get(u, v)); + out('\n'); + } + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/link_cut_tree.vertex_set_path_composite.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/link_cut_tree.vertex_set_path_composite.test.cpp new file mode 100644 index 0000000..5ab4403 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/link_cut_tree.vertex_set_path_composite.test.cpp @@ -0,0 +1,63 @@ +#define PROBLEM "https://judge.yosupo.jp/problem/vertex_set_path_composite" +#include "../data_structure/link_cut_tree.hpp" +#include "../modulus/mint.hpp" +#include "../monoids/linear_function.hpp" +#include "../monoids/dual.hpp" +#include "../utils/macros.hpp" +#include "../hack/fastio.hpp" +#include +#include +using namespace std; + +constexpr int MOD = 998244353; +int main() { + int n = in(); + int q = in(); + + // initialize + link_cut_tree > > > lct(n); + REP (i, n) { + mint a_i = in(); + mint b_i = in(); + lct.vertex_set(i, make_pair(a_i, b_i)); + } + vector > g(n); + REP (i, n - 1) { + int u = in(); + int v = in(); + g[u].push_back(v); + g[v].push_back(u); + } + vector used(n); + stack stk; + stk.push(0); + used[0] = true; + while (not stk.empty()) { + int u = stk.top(); + stk.pop(); + for (int v : g[u]) if (not used[v]) { + lct.link(v, u); + stk.push(v); + used[v] = true; + } + } + + // answer to queries + while (q --) { + int t = in(); + if (t == 0) { + int p = in(); + mint c = in(); + mint d = in(); + lct.vertex_set(p, make_pair(c, d)); + } else if (t == 1) { + int u = in(); + int v = in(); + mint x = in(); + auto [a, b] = lct.path_get(u, v); + out((a * x + b).value); + out('\n'); + } + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/reporting_segment_tree.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/reporting_segment_tree.hpp new file mode 100644 index 0000000..af1cacc --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/reporting_segment_tree.hpp @@ -0,0 +1,75 @@ +#pragma once +#include +#include +#include +#include +#include +#include "../utils/macros.hpp" + +/** + * @brief Dual Segment Tree / 双対セグメント木 (列挙クエリ, 完全二分木) + * @note This tree is very similar to the Bentley's original segment tree. + */ +template +struct reporting_segment_tree { + int size; + std::vector > data; + reporting_segment_tree() = default; + explicit reporting_segment_tree(int size_) { + size = 1; while (size < size_) size *= 2; + data.resize(2 * size - 1); + } + + /** + * @arg key must be unique + * @note $O(\log n)$ + */ + void add_segment(int l, int r, const Key & key) { + assert (0 <= l and l <= r and r <= size); + for (l += size, r += size; l < r; l /= 2, r /= 2) { // 1-based + if (l % 2 == 1) data[(l ++) - 1].push_back(key); + if (r % 2 == 1) data[(-- r) - 1].push_back(key); + } + } + + /** + * @note $O(\log n + k)$ + */ + template + void list_segments(int i, Callback & callback) { + static_assert (std::is_invocable_r::value, ""); + assert (0 <= i and i < size); + for (i += size; i > 0; i /= 2) { // 1-based + for (const auto & key : data[i - 1]) { + callback(key); + } + } + } + + /** + * @note $O(n + k)$ + * @arg remove can be implemented as undo + * @arg report is called with all indices in increasing order + */ + template + void traverse_segments(Add & add, Remove & remove, Report & report) { + static_assert (std::is_invocable_r::value, ""); + static_assert (std::is_invocable_r::value, ""); + static_assert (std::is_invocable_r::value, ""); + std::function dfs = [&](int i, int l, int r) { + for (const auto & key : data[i]) { + add(key); + } + if (i >= size - 1) { + report(i - size + 1); + } else { + dfs(2 * i + 1, l, (l + r) / 2); + dfs(2 * i + 2, (l + r) / 2, r); + } + for (auto it = data[i].rbegin(); it != data[i].rend(); ++ it) { + remove(*it); + } + }; + dfs(0, 0, size); + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree.hpp new file mode 100644 index 0000000..1b30478 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree.hpp @@ -0,0 +1,93 @@ +#pragma once +#include +#include +#include +#include "../utils/macros.hpp" + +/** + * @brief Segment Tree / セグメント木 (monoids, 完全二分木) + * @docs data_structure/segment_tree.md + * @tparam Monoid (commutativity is not required) + */ +template +struct segment_tree { + typedef typename Monoid::value_type value_type; + Monoid mon; + int n; + std::vector a; + segment_tree() = default; + segment_tree(int n_, const Monoid & mon_ = Monoid()) : mon(mon_) { + n = 1; while (n < n_) n *= 2; + a.resize(2 * n - 1, mon.unit()); + } + void point_set(int i, value_type b) { // 0-based + assert (0 <= i and i < n); + a[i + n - 1] = b; + for (i = (i + n) / 2; i > 0; i /= 2) { // 1-based + a[i - 1] = mon.mult(a[2 * i - 1], a[2 * i]); + } + } + value_type range_get(int l, int r) { // 0-based, [l, r) + assert (0 <= l and l <= r and r <= n); + value_type lacc = mon.unit(), racc = mon.unit(); + for (l += n, r += n; l < r; l /= 2, r /= 2) { // 1-based loop, 2x faster than recursion + if (l % 2 == 1) lacc = mon.mult(lacc, a[(l ++) - 1]); + if (r % 2 == 1) racc = mon.mult(a[(-- r) - 1], racc); + } + return mon.mult(lacc, racc); + } + + value_type point_get(int i) { // 0-based + assert (0 <= i and i < n); + return a[i + n - 1]; + } + + /** + * @note O(min(n, (r - l) log n)) + */ + void range_set(int l, int r, value_type b) { + assert (0 <= l and l <= r and r <= n); + range_set(0, 0, n, l, r, b); + } + void range_set(int i, int il, int ir, int l, int r, value_type b) { + if (l <= il and ir <= r and ir - il == 1) { // 0-based + a[i] = b; + } else if (ir <= l or r <= il) { + // nop + } else { + range_set(2 * i + 1, il, (il + ir) / 2, l, r, b); + range_set(2 * i + 2, (il + ir) / 2, ir, l, r, b); + a[i] = mon.mult(a[2 * i + 1], a[2 * i + 2]); + } + } + + /** + * @brief a fast & semigroup-friendly version constructor + * @note $O(n)$ + */ + template + segment_tree(InputIterator first, InputIterator last, const Monoid & mon_ = Monoid()) : mon(mon_) { + int size = std::distance(first, last); + n = 1; while (n < size) n *= 2; + a.resize(2 * n - 1, mon.unit()); + std::copy(first, last, a.begin() + (n - 1)); + unsafe_rebuild(); + } + /** + * @brief update a leaf node without updating ancestors + * @note $O(1)$ + */ + void unsafe_point_set(int i, value_type b) { // 0-based + assert (0 <= i and i < n); + a[i + n - 1] = b; + } + /** + * @brief re-build non-leaf nodes from leaf nodes + * @note $O(n)$ + */ + void unsafe_rebuild() { + REP_R (i, n - 1) { + a[i] = mon.mult(a[2 * i + 1], a[2 * i + 2]); + } + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree.md b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree.md new file mode 100644 index 0000000..9235990 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree.md @@ -0,0 +1,6 @@ +## 概要 + +monoid $M = (M, \cdot, 1)$ の要素の列 $a = (a_0, a_1, \dots, a _ {n - 1}) \in M^n$ に対し、次が $O(\log N)$ で処理可能: + +- $\mathtt{point\unicode{95}set}(i, b)$: $a_i \gets b$ と更新する。 +- $\mathtt{range\unicode{95}get}(l, r)$: 積 $a_l \cdot a _ {l + 1} \cdot \dots \cdot a _ {r - 1}$ を計算する。 diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree.point_add_range_sum.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree.point_add_range_sum.test.cpp new file mode 100644 index 0000000..06090de --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree.point_add_range_sum.test.cpp @@ -0,0 +1,28 @@ +#define PROBLEM "https://judge.yosupo.jp/problem/point_add_range_sum" +#include "../data_structure/segment_tree.hpp" +#include "../monoids/plus.hpp" +#include "../utils/macros.hpp" +#include +#include +#include +#include +using namespace std; + +int main() { + int n, q; scanf("%d%d", &n, &q); + vector a(n); + REP (i, n) { + scanf("%lld", &a[i]); + } + segment_tree > segtree(ALL(a)); + while (q --) { + int t, x, y; scanf("%d%d%d", &t, &x, &y); + if (t == 0) { + segtree.point_set(x, segtree.point_get(x) + y); + } else if (t == 1) { + int64_t answer = segtree.range_get(x, y); + printf("%lld\n", answer); + } + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree.point_set_range_composite.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree.point_set_range_composite.test.cpp new file mode 100644 index 0000000..1f72456 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree.point_set_range_composite.test.cpp @@ -0,0 +1,29 @@ +#define PROBLEM "https://judge.yosupo.jp/problem/point_set_range_composite" +#include "../data_structure/segment_tree.hpp" +#include "../monoids/linear_function.hpp" +#include "../monoids/dual.hpp" +#include "../modulus/mint.hpp" +#include "../utils/macros.hpp" +#include +#include +using namespace std; + +constexpr int MOD = 998244353; +int main() { + int n, q; scanf("%d%d", &n, &q); + segment_tree > > > segtree(n); + REP (i, n) { + int a, b; scanf("%d%d", &a, &b); + segtree.point_set(i, make_pair(a, b)); + } + while (q --) { + int f, x, y, z; scanf("%d%d%d%d", &f, &x, &y, &z); + if (f == 0) { + segtree.point_set(x, make_pair(y, z)); + } else if (f == 1) { + mint a, b; tie(a, b) = segtree.range_get(x, y); + printf("%d\n", (a * z + b).value); + } + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree.range_sum_query.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree.range_sum_query.test.cpp new file mode 100644 index 0000000..378e382 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree.range_sum_query.test.cpp @@ -0,0 +1,21 @@ +#define PROBLEM "http://judge.u-aizu.ac.jp/onlinejudge/description.jsp?id=DSL_2_B" +#include "../data_structure/segment_tree.hpp" +#include "../monoids/plus.hpp" + +#include +using namespace std; + +int main() { + int n, q; cin >> n >> q; + segment_tree > segtree(n); + while (q --) { + int com, x, y; cin >> com >> x >> y; + -- x; + if (com == 0) { + segtree.point_set(x, segtree.range_get(x, x + 1) + y); + } else if (com == 1) { + cout << segtree.range_get(x, y) << endl; + } + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree_beats.DSL_2_F.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree_beats.DSL_2_F.test.cpp new file mode 100644 index 0000000..9fa5912 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree_beats.DSL_2_F.test.cpp @@ -0,0 +1,32 @@ +#define PROBLEM "http://judge.u-aizu.ac.jp/onlinejudge/description.jsp?id=DSL_2_F" +#include "../data_structure/segment_tree_beats.hpp" + +#include +#include +#include "../utils/macros.hpp" +#include "../hack/fastio.hpp" + +int main() { + int n = in(); + int q = in(); + + constexpr int64_t initial = (1ull << 31) - 1; + segment_tree_beats beats(n); + beats.range_update(0, n, initial); + + while (q --) { + int ty = in(); + int l = in(); + int r = in(); + ++ r; + if (ty == 0) { + int64_t x = in(); + beats.range_update(l, r, x); + } else { + int64_t min = beats.range_min(l, r); + out(min); + out('\n'); + } + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree_beats.DSL_2_G.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree_beats.DSL_2_G.test.cpp new file mode 100644 index 0000000..5bfdcf1 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree_beats.DSL_2_G.test.cpp @@ -0,0 +1,30 @@ +#define PROBLEM "http://judge.u-aizu.ac.jp/onlinejudge/description.jsp?id=DSL_2_G" +#include "../data_structure/segment_tree_beats.hpp" + +#include +#include +#include "../utils/macros.hpp" +#include "../hack/fastio.hpp" + +int main() { + int n = in(); + int q = in(); + + segment_tree_beats beats(n); + + while (q --) { + int ty = in(); + int l = in(); + int r = in(); + -- l; + if (ty == 0) { + int64_t x = in(); + beats.range_add(l, r, x); + } else { + int64_t sum = beats.range_sum(l, r); + out(sum); + out('\n'); + } + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree_beats.DSL_2_H.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree_beats.DSL_2_H.test.cpp new file mode 100644 index 0000000..da9ba7e --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree_beats.DSL_2_H.test.cpp @@ -0,0 +1,30 @@ +#define PROBLEM "http://judge.u-aizu.ac.jp/onlinejudge/description.jsp?id=DSL_2_H" +#include "../data_structure/segment_tree_beats.hpp" + +#include +#include +#include "../utils/macros.hpp" +#include "../hack/fastio.hpp" + +int main() { + int n = in(); + int q = in(); + + segment_tree_beats beats(n); + + while (q --) { + int ty = in(); + int l = in(); + int r = in(); + ++ r; + if (ty == 0) { + int64_t x = in(); + beats.range_add(l, r, x); + } else { + int64_t min = beats.range_min(l, r); + out(min); + out('\n'); + } + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree_beats.DSL_2_I.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree_beats.DSL_2_I.test.cpp new file mode 100644 index 0000000..c7d68ce --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree_beats.DSL_2_I.test.cpp @@ -0,0 +1,30 @@ +#define PROBLEM "http://judge.u-aizu.ac.jp/onlinejudge/description.jsp?id=DSL_2_I" +#include "../data_structure/segment_tree_beats.hpp" + +#include +#include +#include "../utils/macros.hpp" +#include "../hack/fastio.hpp" + +int main() { + int n = in(); + int q = in(); + + segment_tree_beats beats(n); + + while (q --) { + int ty = in(); + int l = in(); + int r = in(); + ++ r; + if (ty == 0) { + int64_t x = in(); + beats.range_update(l, r, x); + } else { + int64_t sum = beats.range_sum(l, r); + out(sum); + out('\n'); + } + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree_beats.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree_beats.hpp new file mode 100644 index 0000000..db17847 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree_beats.hpp @@ -0,0 +1,275 @@ +#pragma once +#include +#include +#include +#include +#include +#include "../utils/macros.hpp" + +/** + * @brief Segment Tree Beats (range {chmin, chmax, add, update} + range {min, max, sum}, 完全二分木) + * @docs data_structure/segment_tree_beats.md + * @see https://codeforces.com/blog/entry/57319 + */ +class segment_tree_beats { + // MEMO: values for queries (max, min, lazy_add, and lazy_update) already apply to the current node; but not for children + typedef struct { + int64_t max; + int64_t max_second; + int max_count; + int64_t min; + int64_t min_second; + int min_count; + int64_t lazy_add; + int64_t lazy_update; + int64_t sum; + } value_type; + + int n; + std::vector a; + +public: + segment_tree_beats() = default; + segment_tree_beats(int n_) { + n = 1; while (n < n_) n *= 2; + a.resize(2 * n - 1); + tag(0, 0); + } + template + segment_tree_beats(InputIterator first, InputIterator last) { + int n_ = std::distance(first, last); + n = 1; while (n < n_) n *= 2; + a.resize(2 * n - 1); + REP (i, n_) { + tag(n - 1 + i, *(first + i)); + } + REP3 (i, n_, n) { + tag(n - 1 + i, 0); + } + REP_R (i, n - 1) { + update(i); + } + } + + void range_chmin(int l, int r, int64_t value) { // 0-based, [l, r) + assert (0 <= l and l <= r and r <= n); + range_apply(0, 0, n, l, r, value); + } + void range_chmax(int l, int r, int64_t value) { // 0-based, [l, r) + assert (0 <= l and l <= r and r <= n); + range_apply(0, 0, n, l, r, value); + } + void range_add(int l, int r, int64_t value) { // 0-based, [l, r) + assert (0 <= l and l <= r and r <= n); + range_apply(0, 0, n, l, r, value); + } + void range_update(int l, int r, int64_t value) { // 0-based, [l, r) + assert (0 <= l and l <= r and r <= n); + range_apply(0, 0, n, l, r, value); + } + + int64_t range_min(int l, int r) { // 0-based, [l, r) + assert (0 <= l and l <= r and r <= n); + return range_get(0, 0, n, l, r); + } + int64_t range_max(int l, int r) { // 0-based, [l, r) + assert (0 <= l and l <= r and r <= n); + return range_get(0, 0, n, l, r); + } + int64_t range_sum(int l, int r) { // 0-based, [l, r) + assert (0 <= l and l <= r and r <= n); + return range_get(0, 0, n, l, r); + } + +private: + static constexpr char CHMIN = 0; + static constexpr char CHMAX = 1; + static constexpr char ADD = 2; + static constexpr char UPDATE = 3; + static constexpr char MIN = 10; + static constexpr char MAX = 11; + static constexpr char SUM = 12; + + template + void range_apply(int i, int il, int ir, int l, int r, int64_t g) { + if (ir <= l or r <= il or break_condition(i, g)) { + // break + } else if (l <= il and ir <= r and tag_condition(i, g)) { + tag(i, g); + } else { + pushdown(i); + range_apply(2 * i + 1, il, (il + ir) / 2, l, r, g); + range_apply(2 * i + 2, (il + ir) / 2, ir, l, r, g); + update(i); + } + } + template + inline bool break_condition(int i, int64_t g) { + switch (TYPE) { + case CHMIN: return a[i].max <= g; + case CHMAX: return g <= a[i].min; + case ADD: return false; + case UPDATE: return false; + default: assert (false); + } + } + template + inline bool tag_condition(int i, int64_t g) { + switch (TYPE) { + case CHMIN: return a[i].max_second < g and g < a[i].max; + case CHMAX: return a[i].min < g and g < a[i].min_second; + case ADD: return true; + case UPDATE: return true; + default: assert (false); + } + } + template + inline void tag(int i, int64_t g) { + int length = n >> (32 - __builtin_clz(i + 1) - 1); + if (TYPE == CHMIN) { + if (a[i].max == a[i].min or g <= a[i].min) { + tag(i, g); + return; + } + if (a[i].max != INT64_MIN) { + a[i].sum -= a[i].max * a[i].max_count; + } + a[i].max = g; + a[i].min_second = std::min(a[i].min_second, g); + if (a[i].lazy_update != INT64_MAX) { + a[i].lazy_update = std::min(a[i].lazy_update, g); + } + a[i].sum += g * a[i].max_count; + } else if (TYPE == CHMAX) { + if (a[i].max == a[i].min or a[i].max <= g) { + tag(i, g); + return; + } + if (a[i].min != INT64_MAX) { + a[i].sum -= a[i].min * a[i].min_count; + } + a[i].min = g; + a[i].max_second = std::max(a[i].max_second, g); + if (a[i].lazy_update != INT64_MAX) { + a[i].lazy_update = std::max(a[i].lazy_update, g); + } + a[i].sum += g * a[i].min_count; + } else if (TYPE == ADD) { + if (a[i].max != INT64_MAX) { + a[i].max += g; + } + if (a[i].max_second != INT64_MIN) { + a[i].max_second += g; + } + if (a[i].min != INT64_MIN) { + a[i].min += g; + } + if (a[i].min_second != INT64_MAX) { + a[i].min_second += g; + } + a[i].lazy_add += g; + if (a[i].lazy_update != INT64_MAX) { + a[i].lazy_update += g; + } + a[i].sum += g * length; + } else if (TYPE == UPDATE) { + a[i].max = g; + a[i].max_second = INT64_MIN; + a[i].max_count = length; + a[i].min = g; + a[i].min_second = INT64_MAX; + a[i].min_count = length; + a[i].lazy_add = 0; + a[i].lazy_update = INT64_MAX; + a[i].sum = g * length; + } else { + assert (false); + } + } + void pushdown(int i) { + int l = 2 * i + 1; + int r = 2 * i + 2; + // update + if (a[i].lazy_update != INT64_MAX) { + tag(l, a[i].lazy_update); + tag(r, a[i].lazy_update); + a[i].lazy_update = INT64_MAX; + return; + } + // add + if (a[i].lazy_add != 0) { + tag(l, a[i].lazy_add); + tag(r, a[i].lazy_add); + a[i].lazy_add = 0; + } + // chmin + if (a[i].max < a[l].max) { + tag(l, a[i].max); + } + if (a[i].max < a[r].max) { + tag(r, a[i].max); + } + // chmax + if (a[l].min < a[i].min) { + tag(l, a[i].min); + } + if (a[r].min < a[i].min) { + tag(r, a[i].min); + } + } + void update(int i) { + int l = 2 * i + 1; + int r = 2 * i + 2; + // chmin + std::vector b { a[l].max, a[l].max_second, a[r].max, a[r].max_second }; + std::sort(b.rbegin(), b.rend()); + b.erase(std::unique(ALL(b)), b.end()); + a[i].max = b[0]; + a[i].max_second = b[1]; + a[i].max_count = (b[0] == a[l].max ? a[l].max_count : 0) + (b[0] == a[r].max ? a[r].max_count : 0); + // chmax + std::vector c { a[l].min, a[l].min_second, a[r].min, a[r].min_second }; + std::sort(ALL(c)); + c.erase(std::unique(ALL(c)), c.end()); + a[i].min = c[0]; + a[i].min_second = c[1]; + a[i].min_count = (c[0] == a[l].min ? a[l].min_count : 0) + (c[0] == a[r].min ? a[r].min_count : 0); + // add + a[i].lazy_add = 0; + // update + a[i].lazy_update = INT64_MAX; + // sum + a[i].sum = a[l].sum + a[r].sum; + } + + template + int64_t range_get(int i, int il, int ir, int l, int r) { + if (ir <= l or r <= il) { + switch (TYPE) { + case MIN: return INT64_MAX; + case MAX: return INT64_MIN; + case SUM: return 0; + default: assert (false); + } + } else if (l <= il and ir <= r) { + // base + switch (TYPE) { + case MIN: return a[i].min; + case MAX: return a[i].max; + case SUM: return a[i].sum; + default: assert (false); + } + } else { + pushdown(i); + int64_t value_l = range_get(2 * i + 1, il, (il + ir) / 2, l, r); + int64_t value_r = range_get(2 * i + 2, (il + ir) / 2, ir, l, r); + // mult + switch (TYPE) { + case MIN: return std::min(value_l, value_r); + case MAX: return std::max(value_l, value_r); + case SUM: return value_l + value_r; + default: assert (false); + } + } + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree_beats.md b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree_beats.md new file mode 100644 index 0000000..ede2162 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree_beats.md @@ -0,0 +1,16 @@ +## 概要 + +整数 $\mathbb{Z} = (\mathbb{Z}, +, 0, \le)$ の要素の列 $a = (a_0, a_1, \dots, a _ {n - 1}) \in \mathbb{Z}^n$ に対し、次が $O((\log N)^2)$ amortized (一部は $O(\log N)$) で処理可能: + +- $\mathtt{range\unicode{95}chmin}(l, r, b)$: それぞれの $i \in \lbrack l, r)$ に対し $a_i \gets \min(a_i, b)$ と更新する。 +- $\mathtt{range\unicode{95}chmax}(l, r, b)$: それぞれの $i \in \lbrack l, r)$ に対し $a_i \gets \max(a_i, b)$ と更新する。 +- $\mathtt{range\unicode{95}add}(l, r, b)$: それぞれの $i \in \lbrack l, r)$ に対し $a_i \gets a_i + b$ と更新する。 +- $\mathtt{range\unicode{95}update}(l, r, b)$: それぞれの $i \in \lbrack l, r)$ に対し $a_i \gets b$ と更新する。 +- $\mathtt{range\unicode{95}min}(l, r)$: $\min _ {i \in [l, r)} a_i$ を計算する。 +- $\mathtt{range\unicode{95}max}(l, r)$: $\max _ {i \in [l, r)} a_i$ を計算する。 +- $\mathtt{range\unicode{95}sum}(l, r)$: $\sum _ {i \in [l, r)} a_i$ を計算する。 + +他にも: + +- $\mathtt{point\unicode{95}set}(i, b)$: $a_i \gets b$ と更新する。 +- $\mathtt{point\unicode{95}get}(i)$: 値 $a_i$ を計算する。 diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree_beats.yosupo.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree_beats.yosupo.test.cpp new file mode 100644 index 0000000..419354c --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/segment_tree_beats.yosupo.test.cpp @@ -0,0 +1,39 @@ +#define PROBLEM "https://judge.yosupo.jp/problem/range_chmin_chmax_add_range_sum" +#include "../data_structure/segment_tree_beats.hpp" + +#include +#include +#include "../utils/macros.hpp" +#include "../hack/fastio.hpp" + +int main() { + int n = in(); + int q = in(); + + std::vector a(n); + REP (i, n) { + a[i] = in(); + } + segment_tree_beats beats(ALL(a)); + + while (q --) { + int ty = in(); + int l = in(); + int r = in(); + if (ty == 0) { + int64_t b = in(); + beats.range_chmin(l, r, b); + } else if (ty == 1) { + int64_t b = in(); + beats.range_chmax(l, r, b); + } else if (ty == 2) { + int64_t b = in(); + beats.range_add(l, r, b); + } else { + int64_t sum = beats.range_sum(l, r); + out(sum); + out('\n'); + } + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/sliding_window_aggregation.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/sliding_window_aggregation.hpp new file mode 100644 index 0000000..b47f617 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/sliding_window_aggregation.hpp @@ -0,0 +1,57 @@ +#pragma once +#include +#include +#include +#include "../utils/macros.hpp" + +/** + * @brief Sliding Window Aggregation / 含まれる要素の総和が $O(1)$ で取れる queue (可換とは限らない monoid が乗る) + */ +template +struct sliding_window_aggregation { + typedef typename Monoid::value_type value_type; + Monoid mon; + std::deque data; + int front; + value_type back; + sliding_window_aggregation(const Monoid & mon_ = Monoid()) : mon(mon_) { + front = 0; + back = mon.unit(); + } + /** + * @note O(1) + */ + void push(value_type x) { + data.push_back(x); + back = mon.mult(back, x); + } + /** + * @note amortized O(1) + */ + void pop() { + assert (not data.empty()); + data.pop_front(); + if (front) { + -- front; + } else { + REP_R (i, (int)data.size() - 1) { + data[i] = mon.mult(data[i], data[i + 1]); + } + front = data.size(); + back = mon.unit(); + } + } + /** + * @brief get sum of elements in the queue + * @note O(1) + */ + value_type accumulate() const { + return front ? mon.mult(data.front(), back) : back; + } + bool empty() const { + return data.empty(); + } + std::size_t size() const { + return data.size(); + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/sliding_window_aggregation.yosupo.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/sliding_window_aggregation.yosupo.test.cpp new file mode 100644 index 0000000..158add2 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/sliding_window_aggregation.yosupo.test.cpp @@ -0,0 +1,28 @@ +#define PROBLEM "https://judge.yosupo.jp/problem/queue_operate_all_composite" +#include "../data_structure/sliding_window_aggregation.hpp" +#include "../monoids/linear_function.hpp" +#include "../monoids/dual.hpp" +#include "../modulus/mint.hpp" +#include +#include +using namespace std; + +constexpr int MOD = 998244353; +int main() { + int q; scanf("%d", &q); + sliding_window_aggregation > > > swag; + while (q --) { + int t; scanf("%d", &t); + if (t == 0) { + int a, b; scanf("%d%d", &a, &b); + swag.push(make_pair(a, b)); + } else if (t == 1) { + swag.pop(); + } else if (t == 2) { + int x; scanf("%d", &x); + mint a, b; tie(a, b) = swag.accumulate(); + printf("%d\n", (a * x + b).value); + } + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/sparse_table.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/sparse_table.hpp new file mode 100644 index 0000000..eee6cba --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/sparse_table.hpp @@ -0,0 +1,46 @@ +#pragma once +#include +#include +#include "../utils/macros.hpp" + +/** + * @brief Sparse Table (idempotent monoid) + * @note the unit is required just for convenience + * @note $O(N \log N)$ space + */ +template +struct sparse_table { + typedef typename IdempotentMonoid::value_type value_type; + std::vector > table; + IdempotentMonoid mon; + sparse_table() = default; + + /** + * @note $O(N \log N)$ time + */ + template + sparse_table(InputIterator first, InputIterator last, const IdempotentMonoid & mon_ = IdempotentMonoid()) + : mon(mon_) { + table.emplace_back(first, last); + int n = table[0].size(); + int log_n = 32 - __builtin_clz(n); + table.resize(log_n, std::vector(n)); + REP (k, log_n - 1) { + REP (i, n) { + table[k + 1][i] = i + (1ll << k) < n ? + mon.mult(table[k][i], table[k][i + (1ll << k)]) : + table[k][i]; + } + } + } + + /** + * @note $O(1)$ + */ + value_type range_get(int l, int r) const { + if (l == r) return mon.unit(); // if there is no unit, remove this line + assert (0 <= l and l < r and r <= (int)table[0].size()); + int k = 31 - __builtin_clz(r - l); // log2 + return mon.mult(table[k][l], table[k][r - (1ll << k)]); + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/sparse_table.yosupo.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/sparse_table.yosupo.test.cpp new file mode 100644 index 0000000..2541230 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/sparse_table.yosupo.test.cpp @@ -0,0 +1,28 @@ +#define PROBLEM "https://judge.yosupo.jp/problem/staticrmq" +#include "../data_structure/sparse_table.hpp" + +#include "../utils/macros.hpp" +#include "../monoids/min.hpp" +#include +#include +using namespace std; + +int main() { + // input a sequence + int n, q; scanf("%d%d", &n, &q); + vector a(n); + REP (i, n) { + scanf("%d", &a[i]); + } + + // construct the sparse table + sparse_table > st(ALL(a)); + + // answer to queries + while (q --) { + int l, r; scanf("%d%d", &l, &r); + int answer = st.range_get(l, r); + printf("%d\n", answer); + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/sparse_table.yukicoder-1036.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/sparse_table.yukicoder-1036.test.cpp new file mode 100644 index 0000000..eacc0b0 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/sparse_table.yukicoder-1036.test.cpp @@ -0,0 +1,32 @@ +#define PROBLEM "https://yukicoder.me/problems/no/1036" +#include +#include +#include "../utils/macros.hpp" +#include "../data_structure/sparse_table.hpp" +#include "../monoids/gcd.hpp" + +long long solve(int n, const std::vector & a) { + sparse_table > table(ALL(a)); + int r = 0; + long long ans = 0; + REP (l, n) { + while (r < n and table.range_get(l, r) != 1) { + ++ r; + } + if (r == n and table.range_get(l, r) != 1) { + break; + } + ans += n - r + 1; + } + return ans; +} + +int main() { + int n; scanf("%d", &n); + std::vector a(n); + REP (i, n) { + scanf("%lld", &a[i]); + } + printf("%lld\n", solve(n, a)); + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/union_find_tree.aoj.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/union_find_tree.aoj.test.cpp new file mode 100644 index 0000000..5f05f3a --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/union_find_tree.aoj.test.cpp @@ -0,0 +1,18 @@ +#define PROBLEM "http://judge.u-aizu.ac.jp/onlinejudge/description.jsp?id=DSL_1_A" +#include "../data_structure/union_find_tree.hpp" +#include + +int main() { + int n, q; scanf("%d%d", &n, &q); + union_find_tree uft(n); + while (q --) { + int com, x, y; scanf("%d%d%d", &com, &x, &y); + if (com == 0) { + uft.unite_trees(x, y); + } else if (com == 1) { + bool answer = uft.is_same(x, y); + printf("%d\n", (int)answer); + } + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/union_find_tree.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/union_find_tree.hpp new file mode 100644 index 0000000..c531dc6 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/union_find_tree.hpp @@ -0,0 +1,26 @@ +#pragma once +#include + +/** + * @brief Union-Find Tree + * @docs data_structure/union_find_tree.md + * @note union-by-size + path-compression + */ +struct union_find_tree { + std::vector data; + union_find_tree() = default; + explicit union_find_tree(std::size_t n) : data(n, -1) {} + bool is_root(int i) { return data[i] < 0; } + int find_root(int i) { return is_root(i) ? i : (data[i] = find_root(data[i])); } + int tree_size(int i) { return - data[find_root(i)]; } + int unite_trees(int i, int j) { + i = find_root(i); j = find_root(j); + if (i != j) { + if (tree_size(i) < tree_size(j)) std::swap(i, j); + data[i] += data[j]; + data[j] = i; + } + return i; + } + bool is_same(int i, int j) { return find_root(i) == find_root(j); } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/union_find_tree.md b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/union_find_tree.md new file mode 100644 index 0000000..dd611a1 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/union_find_tree.md @@ -0,0 +1,9 @@ +## 概要 + +頂点数 $N$ で辺数 $0$ の無向グラフ $G = (V, E)$ に対し、次が $O(\alpha(N))$ amortized (ただし $\alpha$ は Ackermann 関数の逆関数) で処理可能。 + +- $\mathtt{unite\unicode{95}trees}(u, v)$: 頂点 $u, v$ 間に辺を追加する。 + - $u, v$ 間の辺は無向であることに注意 +- $\mathtt{find\unicode{95}root}(v)$: 頂点 $v$ の所属する連結成分の代表元を取得する。 +- $\mathtt{is\unicode{95}same}(u, v)$: 頂点 $u, v$ が同じ連結成分に所属するかを判定する。 +- $\mathtt{tree\unicode{95}size}(v)$: 頂点 $v$ の所属する連結成分の要素数を取得する。 diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/union_find_tree.yosupo.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/union_find_tree.yosupo.test.cpp new file mode 100644 index 0000000..5834ada --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/union_find_tree.yosupo.test.cpp @@ -0,0 +1,18 @@ +#define PROBLEM "https://judge.yosupo.jp/problem/unionfind" +#include "../data_structure/union_find_tree.hpp" +#include + +int main() { + int n, q; scanf("%d%d", &n, &q); + union_find_tree uft(n); + while (q --) { + int t, u, v; scanf("%d%d%d", &t, &u, &v); + if (t == 0) { + uft.unite_trees(u, v); + } else if (t == 1) { + bool answer = uft.is_same(u, v); + printf("%d\n", (int)answer); + } + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/union_find_tree_foldable_undoable.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/union_find_tree_foldable_undoable.hpp new file mode 100644 index 0000000..b951ebe --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/union_find_tree_foldable_undoable.hpp @@ -0,0 +1,70 @@ +#pragma once +#include +#include +#include +#include + +/** + * @brief Union-Find Tree (foldable with commutative monoids, undoable) + * @note $O(\log N)$ + * @note union-by-size (without path-compression) + */ +template +class union_find_tree_foldable_undoable { + typedef typename CommutativeMonoid::value_type value_type; + const CommutativeMonoid mon; + std::vector data; + std::vector value; + std::vector > history; + +public: + union_find_tree_foldable_undoable() = default; + union_find_tree_foldable_undoable(std::size_t size, const CommutativeMonoid & mon_ = CommutativeMonoid()) + : mon(mon_), data(size, -1), value(size, mon.unit()) { + } + template + union_find_tree_foldable_undoable(InputIterator first, InputIterator last, const CommutativeMonoid & mon_ = CommutativeMonoid()) + : mon(mon_), data(std::distance(first, last), -1), value(first, last) { + } + + bool is_root(int i) { return data[i] < 0; } + int find_root(int i) { while (not is_root(i)) i = data[i]; return i; } + int get_size(int i) { return - data[find_root(i)]; } + void unite_trees(int i, int j) { + i = find_root(i); + j = find_root(j); + if (get_size(i) < get_size(j)) std::swap(i, j); + history.emplace_back(-1, 0, mon.unit()); + if (i != j) { + history.emplace_back(i, data[i], value[i]); + history.emplace_back(j, data[j], value[j]); + data[i] += data[j]; + data[j] = i; + value[i] = mon.mult(value[i], value[j]); + } + } + bool is_connected(int i, int j) { return find_root(i) == find_root(j); } + + void tree_set(int i, value_type x) { + i = find_root(i); + history.emplace_back(-1, 0, mon.unit()); + history.emplace_back(i, data[i], value[i]); + value[i] = x; + } + value_type tree_get(int i) { + return value[find_root(i)]; + } + /** + * @note for unite_trees() and tree_set() + */ + void undo() { + while (true) { + assert (not history.empty()); + auto [i, data_i, value_i] = history.back(); + history.pop_back(); + if (i == -1) break; + value[i] = value_i; + data[i] = data_i; + } + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/union_find_tree_with_monoid.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/union_find_tree_with_monoid.hpp new file mode 100644 index 0000000..fd0136e --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/union_find_tree_with_monoid.hpp @@ -0,0 +1,33 @@ +#pragma once +#include + +/** + * @brief a disjoint set structure with monoid + * @note union-by-size + path-compression + */ +template +struct union_find_tree_with_monoid { + typedef typename Monoid::value_type value_type; + const Monoid mon; + std::vector data; + std::vector value; + + union_find_tree_with_monoid() = default; + union_find_tree_with_monoid(std::size_t n, Monoid const & mon_ = Monoid()) : mon(mon_), data(n, -1), value(n, mon.unit()) {} + bool is_root(int i) { return data[i] < 0; } + int find_root(int i) { return is_root(i) ? i : (data[i] = find_root(data[i])); } + int tree_size(int i) { return - data[find_root(i)]; } + int unite_trees(int i, int j) { + i = find_root(i); j = find_root(j); + if (i != j) { + if (tree_size(i) < tree_size(j)) std::swap(i, j); + data[i] += data[j]; + data[j] = i; + value[i] = mon.mult(value[i], value[j]); + } + return i; + } + bool is_same(int i, int j) { return find_root(i) == find_root(j); } + value_type get_value(int i) { return value[find_root(i)]; } + void set_value(int i, value_type z) { value[find_root(i)] = z; } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/wavelet_matrix.aoj2674.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/wavelet_matrix.aoj2674.test.cpp new file mode 100644 index 0000000..bf229a0 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/wavelet_matrix.aoj2674.test.cpp @@ -0,0 +1,37 @@ +#define PROBLEM "http://judge.u-aizu.ac.jp/onlinejudge/description.jsp?id=2674" +#include "../data_structure/wavelet_matrix.hpp" +#include "../utils/macros.hpp" +#include +#include +using namespace std; + +int main() { + constexpr int BITS = 30; + constexpr int MIN_X = -1e8; + constexpr int MAX_X = 1e8; + + // read a sequence + int d; scanf("%d", &d); + vector x(d); + REP (i, d) { + scanf("%d", &x[i]); + x[i] -= MIN_X; + } + + // construct a wavelet matrix + wavelet_matrix wm(x); + + // answer to queries + int q; scanf("%d", &q); + while (q --) { + int l, r, e; scanf("%d%d%d", &l, &r, &e); + -- l; + int a = min(x[l], x[r - 1]); + int b = max(x[l], x[r - 1]); + int cnt = 0; + cnt += wm.range_frequency(l, r, 0, max(0, a - e)); + cnt += wm.range_frequency(l, r, min(MAX_X - MIN_X + 1, b + e + 1), MAX_X - MIN_X + 1); + printf("%d\n", cnt); + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/wavelet_matrix.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/wavelet_matrix.hpp new file mode 100644 index 0000000..72ae492 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/wavelet_matrix.hpp @@ -0,0 +1,192 @@ +#pragma once +#include +#include +#include +#include +#include +#include "../data_structure/fully_indexable_dictionary.hpp" +#include "../utils/macros.hpp" + + +/** + * @brief Wavelet Matrix + * @docs data_structure/wavelet_matrix.md + * @tparam BITS express the range [0, 2^BITS) of values. You can assume BITS \le \log N, using coordinate compression + * @see https://www.slideshare.net/pfi/ss-15916040 + */ +template +struct wavelet_matrix { + static_assert (BITS < CHAR_BIT * sizeof(uint64_t), ""); + std::array fid; + std::array zero_count; + + wavelet_matrix() = default; + /** + * @note O(N BITS) + */ + template + wavelet_matrix(std::vector data) { + int size = data.size(); + REP (i, size) { + assert (0 <= data[i] and data[i] < (1ull << BITS)); + } + // bit-inversed radix sort + std::vector bits(size); + std::vector next(size); + REP_R (k, BITS) { + auto low = next.begin(); + auto high = next.rbegin(); + REP (i, size) { + bits[i] = bool(data[i] & (1ull << k)); + (bits[i] ? *(high ++) : *(low ++)) = data[i]; + } + fid[k] = fully_indexable_dictionary(bits); + zero_count[k] = low - next.begin(); + reverse(next.rbegin(), high); + data.swap(next); + } + } + + /** + * @brief count the occurrences of value in [l, r) + * @note O(BITS) + * @note even if l = 0, of course the final [l, r) is not always [0, r) + */ + int rank(uint64_t value, int l, int r) const { + assert (0 <= value and value < (1ull << BITS)); + assert (0 <= l and l <= r and r <= fid[0].size); + REP_R (k, BITS) { + bool p = value & (1ull << k); + l = fid[k].rank(p, l) + p * zero_count[k]; + r = fid[k].rank(p, r) + p * zero_count[k]; + } + return r - l; + } + int rank(uint64_t value, int r) const { + return rank(value, 0, r); + } + + /** + * @brief find the index of the k-th occurence of value + * @note O(BITS SELECT) when FID's select() is O(SELECT) + */ + int select(uint64_t value, int k) const { + assert (0 <= value and value < (1ull << BITS)); + assert (0 <= k); + // do rank(value, 0, size) with logging + std::vector l(BITS + 1), r(BITS + 1); + l[BITS] = 0; + r[BITS] = fid[0].size; + REP_R (d, BITS) { + bool p = value & (1ull << d); + l[d] = fid[d].rank(p, l[d + 1]) + p * zero_count[d]; + r[d] = fid[d].rank(p, r[d + 1]) + p * zero_count[d]; + } + if (r[0] - l[0] <= k) return fid[0].size; + // trace the log inversely + REP (d, BITS) { + bool p = value & (1ull << d); + k = fid[d].select(p, k, l[d + 1]) - l[d + 1]; + } + return k; + } + /** + * @brief find the index of the k-th occurence of value in [l, n) + */ + int select(uint64_t value, int k, int l) const { + return select(value, k + rank(value, l)); + } + + /** + * @brief returns the i-th element + * @note O(BITS) + */ + uint64_t access(int i) const { + assert (0 <= i and i < fid[0].size); + uint64_t acc = 0; + REP_R (k, BITS) { + bool p = fid[k].access(i); + acc |= uint64_t(p) << k; + i = fid[k].rank(p, i) + p * zero_count[k]; + } + return acc; + } + + /** + * @brief find the k-th number in [l, r) + * @note O(BITS) + */ + uint64_t quantile(int k, int l, int r) { + assert (0 <= k); + assert (0 <= l and l <= r and r <= fid[0].size); + if (r - l <= k) return 1ull << BITS; + uint64_t acc = 0; + REP_R (d, BITS) { + int lc = fid[d].rank(1, l); + int rc = fid[d].rank(1, r); + int zero = (r - l) - (rc - lc); + bool p = (k >= zero); + if (p) { + acc |= 1ull << d; + l = lc + zero_count[d]; + r = rc + zero_count[d]; + k -= zero; + } else { + l -= lc; + r -= rc; + } + } + return acc; + } + + /** + * @brief count the number of values in [value_l, value_r) in range [l, r) + * @note O(BITS) + */ + int range_frequency(int l, int r, uint64_t value_l, uint64_t value_r) const { + assert (0 <= l and l <= r and r <= fid[0].size); + assert (0 <= value_l and value_l <= value_r and value_r <= (1ull << BITS)); + return range_frequency(BITS - 1, l, r, 0, value_l, value_r); + } + int range_frequency(int k, int l, int r, uint64_t v, uint64_t a, uint64_t b) const { + if (l == r) return 0; + if (k == -1) return (a <= v and v < b) ? r - l : 0; + uint64_t nv = v | (1ull << k); + uint64_t nnv = nv | ((1ull << k) - 1); + if (nnv < a or b <= v) return 0; + if (a <= v and nnv < b) return r - l; + int lc = fid[k].rank(1, l); + int rc = fid[k].rank(1, r); + return + range_frequency(k - 1, l - lc, r - rc, v, a, b) + + range_frequency(k - 1, lc + zero_count[k], rc + zero_count[k], nv, a, b); + } + + /** + * @brief flexible version of range_frequency, buf a little bit slow + * @note O(K BITS), K is the number of kinds of values in the range + * @arg void callback(uint64_t value, int count) + */ + template + void range_frequency_callback(int l, int r, uint64_t value_l, uint64_t value_r, Func callback) const { + assert (0 <= l and l <= r and r <= fid[0].size); + assert (0 <= value_l and value_l <= value_r and value_r <= (1ull << BITS)); + range_frequency_callback(BITS - 1, l, r, 0, value_l, value_r, callback); + } + template + void range_frequency_callback(int k, int l, int r, uint64_t v, uint64_t a, uint64_t b, Func callback) const { + if (l == r) return; + if (b <= v) return; + if (k == -1) { + if (a <= v) callback(v, r - l); + return; + } + uint64_t nv = v | (1ull << k); + uint64_t nnv = nv | (((1ull << k) - 1)); + if (nnv < a) return; + int lc = fid[k].rank(1, l); + int rc = fid[k].rank(1, r); + range_frequency_callback(k - 1, l - lc, r - rc, v, a, b, callback); + range_frequency_callback(k - 1, lc + zero_count[k], rc + zero_count[k], nv, a, b, callback); + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/wavelet_matrix.md b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/wavelet_matrix.md new file mode 100644 index 0000000..e67bc8a --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/wavelet_matrix.md @@ -0,0 +1,14 @@ +## 概要 + +完備辞書を $K$ 本ならべて $2^K$ 未満の自然数に対応させたような静的データ構造。 + +## Operations + +ある固定された自然数 $K$ と $2^K$ 未満の自然数の列 $a = (a_0, a_1, \dots, a _ {n - 1}) \in (2^K)^N$ に対し、次が $O(K)$ で処理可能: + +- $\mathtt{rank}(b, l, r)$: 区間 $\lbrack l, r)$ 中の値 $b$ の出現回数 $$\unicode{35} \lbrace i \in \lbrack l, r) \mid a_i = b \rbrace$$ を計算する。 +- $\mathtt{select}(b, k, l)$: 位置 $l$ 以降で $k \ge 0$ 番目に出現する値 $b$ の位置 $i$ (つまり $i \ge l$ かつ $a_i = b$ かつ $$\unicode{35} \lbrace j \in \lbrack l, i) \mid a_j = b \rbrace = k$$ を満たす $i$) を計算する。 + - ただし今回の実装では完備辞書側の妥協の結果 $O(K \log N)$ になっている +- $\mathtt{access}(i)$: 値 $a_i$ を計算する。 +- $\mathtt{quantile}(k, l, r)$: 区間 $\lbrack l, r)$ 内で $k \ge 0$ 番目に小さい要素 $a_i$ (つまり $$\unicode{35} \lbrace j \in \lbrack l, r) \mid (a_j, j) \lt (a_i, i) \rbrace = k$$ を満たす $i$) を計算する。 +- $\mathtt{range\unicode{95}frequency}(l, r, b_l, b_r)$: 区間 $\lbrack l, r)$ 内における $b_l$ 以上 $b_r$ 未満の要素の数 $$\unicode{35} \lbrace i \in \lbrack l, r) \mid a_i \in \lbrack b_l, b_r) \rbrace$$ を計算する。 diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/wavelet_matrix.range_kth_smallest.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/wavelet_matrix.range_kth_smallest.test.cpp new file mode 100644 index 0000000..f4a3b6c --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/wavelet_matrix.range_kth_smallest.test.cpp @@ -0,0 +1,30 @@ +#define PROBLEM "https://judge.yosupo.jp/problem/range_kth_smallest" +#include "../data_structure/wavelet_matrix.hpp" +#include "../utils/macros.hpp" +#include "../hack/fastio.hpp" +#include +using namespace std; + +int main() { + // input + int n = in(); + int q = in(); + vector a(n); + REP (i, n) { + a[i] = in(); + } + + // construct + wavelet_matrix<30> wm(a); + + // output + while (q --) { + int l = in(); + int r = in(); + int k = in(); + int32_t b = wm.quantile(k, l, r); + out(b); + out('\n'); + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/wavelet_matrix.rectangle_sum.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/wavelet_matrix.rectangle_sum.test.cpp new file mode 100644 index 0000000..d2d98ce --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/data_structure/wavelet_matrix.rectangle_sum.test.cpp @@ -0,0 +1,81 @@ +#define PROBLEM "https://judge.yosupo.jp/problem/rectangle_sum" +#include "../data_structure/wavelet_matrix.hpp" +#include "../utils/macros.hpp" +#include "../utils/coordinate_compression.hpp" +#include +#include +#include +using namespace std; + +int main() { + // read points + int n, q; scanf("%d%d", &n, &q); + vector x(n); + vector y(n); + vector w(n); + REP (i, n) { + scanf("%d%d%lld", &x[i], &y[i], &w[i]); + } + + // coordinate compression (not so important, 0.5 sec faster) + coordinate_compression compress_x(ALL(x)); + coordinate_compression compress_y(ALL(y)); + constexpr int BITS = 18; + assert (compress_x.data.size() < (1 << BITS)); + assert (compress_y.data.size() < (1 << BITS)); + REP (i, n) { + x[i] = compress_x[x[i]]; + y[i] = compress_y[y[i]]; + } + + // construct wavlet matrices + constexpr int WIDTH = 16; + constexpr int HEIGHT = 8; + vector order(n); + iota(ALL(order), 0); + sort(ALL(order), [&](int i, int j) { + return x[i] < x[j]; + }); + array, HEIGHT> x1; + array, HEIGHT> y1; + for (int i : order) { + long long w_i = w[i]; + for (int k = 0; w_i; ++ k) { + assert (k < HEIGHT); + REP (j, w_i % WIDTH) { + x1[k].push_back(x[i]); + y1[k].push_back(y[i]); + } + w_i /= WIDTH; + } + } + array, HEIGHT> wm; + REP (k, HEIGHT) { + wm[k] = wavelet_matrix(y1[k]); + } + + // answer to queries + vector lx(q), ly(q), rx(q), ry(q); + REP (i, q) { + scanf("%d%d%d%d", &lx[i], &ly[i], &rx[i], &ry[i]); + lx[i] = compress_x[lx[i]]; + rx[i] = compress_x[rx[i]]; + ly[i] = compress_y[ly[i]]; + ry[i] = compress_y[ry[i]]; + } + vector answer(q); + REP_R (k, HEIGHT) { + REP (i, q) { // swap loops to optimize cache (important, 2x faster) + int l = lower_bound(ALL(x1[k]), lx[i]) - x1[k].begin(); + int r = lower_bound(ALL(x1[k]), rx[i]) - x1[k].begin(); + int a = ly[i]; + int b = ry[i]; + answer[i] *= WIDTH; + answer[i] += wm[k].range_frequency(l, r, a, b); + } + } + REP (i, q) { + printf("%lld\n", answer[i]); + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/chmin_chmax_add.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/chmin_chmax_add.hpp new file mode 100644 index 0000000..be517a4 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/chmin_chmax_add.hpp @@ -0,0 +1,47 @@ +#pragma once +#include +#include +#include + +/** + * @note represents the monoid $M = (\lbrace \lambda x. \min(a, \max(b, c + x)) \mid a, b, c \rbrace, \circ, \mathrm{id})$ + */ +template +struct chmin_chmax_add_monoid { + static constexpr MAX = std::numeric_limits::max(); + static constexpr MIN = std::numeric_limits::lowest(); + struct value_type { + long long chmin; + long long chmax; + long long add; + }; + value_type unit() const { + return (value_type) { MAX, MIN, 0 }; + } + value_type mult(value_type a, value_type b) const { + value_type c = b; + // add + if (c.chmin != MAX) { + c.chmin += a.add; + } + if (c.chmax != MIN) { + c.chmax += a.add; + } + c.add += a.add; + // chmax + c.chmin = std::max(a.chmax, c.chmin); + c.chmax = std::max(a.chmax, c.chmax); + // chmin + c.chmin = std::min(a.chmin, c.chmin); + return c; + } + static value_type chmin(T value) { + return (value_type) { value, MIN, 0 }; + } + static value_type chmin(T value) { + return (value_type) { MAX, value, 0 }; + } + static value_type chmin(T value) { + return (value_type) { MAX, MIN, value }; + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/chmin_chmax_add_min_max_action.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/chmin_chmax_add_min_max_action.hpp new file mode 100644 index 0000000..2145a8b --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/chmin_chmax_add_min_max_action.hpp @@ -0,0 +1,22 @@ +#pragma once +#include +#include +#include +#include +#include "../monoids/chmin_chmax_add.hpp" +#include "../monoids/min_max.hpp" + +template +struct chmin_chmax_add_min_max_action { + typedef typename chmin_chmax_add_monoid::value_type F; + typedef typename min_max_monoid::value_type X; + X operator () (F f, X x) const { + if (x.first == std::numeric_limits::max()) { + assert (x.second == std::numeric_limits::lowest()); + return x; + } + T a = std::min(f.chmin, std::max(f.chmax, f.add + x.first)); + T b = std::min(f.chmin, std::max(f.chmax, f.add + x.second)); + return std::make_pair(a, b); + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/dual.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/dual.hpp new file mode 100644 index 0000000..aca0de0 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/dual.hpp @@ -0,0 +1,12 @@ +#pragma once + +/** + * @see http://hackage.haskell.org/package/base/docs/Data-Monoid.html#t:Dual + */ +template +struct dual_monoid { + typedef typename Monoid::value_type value_type; + Monoid base; + value_type unit() const { return base.unit(); } + value_type mult(const value_type & a, const value_type & b) const { return base.mult(b, a); } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/gcd.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/gcd.hpp new file mode 100644 index 0000000..bbd73fa --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/gcd.hpp @@ -0,0 +1,12 @@ +#pragma once +#include "../number/gcd.hpp" + +/** + * @note a semilattice + */ +template +struct gcd_monoid { + typedef Integer value_type; + Integer unit() const { return 0; } + Integer mult(Integer a, Integer b) const { return gcd(a, b); } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/left.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/left.hpp new file mode 100644 index 0000000..e5ca6d2 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/left.hpp @@ -0,0 +1,10 @@ +#pragma once +#include + +template +struct left_monoid { + // typedef std::optional value_type; + typedef std::pair value_type; + value_type unit() const { return std::make_pair(false, T()); } + value_type mult(value_type a, value_type b) const { return a.first ? a : b; } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/left_action.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/left_action.hpp new file mode 100644 index 0000000..051ff8d --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/left_action.hpp @@ -0,0 +1,13 @@ +#pragma once +#include +#include "../monoids/left.hpp" + +/** + * @note lazy_propagation_segment_tree, Monoid, left_action > is the same to dual_segment_tree + */ +template +struct left_action { + typename left_monoid::value_type operator () (typename Monoid::value_type f, typename left_monoid::value_type x) const { + return x.first ? std::make_pair(true, Monoid().mult(f, x.second)) : x; + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/linear_function.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/linear_function.hpp new file mode 100644 index 0000000..e884e81 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/linear_function.hpp @@ -0,0 +1,16 @@ +#pragma once +#include + +template +struct linear_function_monoid { + typedef std::pair value_type; + linear_function_monoid() = default; + value_type unit() const { + return std::make_pair(1, 0); + } + value_type mult(value_type g, value_type f) const { + CommutativeRing fst = g.first * f.first; + CommutativeRing snd = g.second + g.first * f.second; + return std::make_pair(fst, snd); + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/linear_function_plus_count_action.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/linear_function_plus_count_action.hpp new file mode 100644 index 0000000..c2a6efe --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/linear_function_plus_count_action.hpp @@ -0,0 +1,13 @@ +#pragma once +#include +#include "../monoids/linear_function.hpp" + +/** + * @note lazy_propagation_segment_tree, linear_function_monoid, linear_function_plus_count_action > + */ +template +struct linear_function_plus_count_action { + typename plus_count_monoid::value_type operator () (typename linear_function_monoid::value_type f, typename plus_count_monoid::value_type x) const { + return std::make_pair(f.first * x.first + f.second * x.second, x.second); + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/matrix_template.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/matrix_template.hpp new file mode 100644 index 0000000..f97db5c --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/matrix_template.hpp @@ -0,0 +1,14 @@ +#pragma once +#include "../number/matrix_template.hpp" + +template +struct matrix_monoid { + typedef matrix value_type; + value_type f; + value_type unit() const { + return unit_matrix(); + } + value_type mult(const value_type & f, const value_type & g) const { + return f * g; + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/max.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/max.hpp new file mode 100644 index 0000000..f721fca --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/max.hpp @@ -0,0 +1,10 @@ +#pragma once +#include +#include + +template +struct max_monoid { + typedef T value_type; + value_type unit() const { return std::numeric_limits::lowest(); } + value_type mult(value_type a, value_type b) const { return std::max(a, b); } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/max_index.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/max_index.hpp new file mode 100644 index 0000000..1d93c30 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/max_index.hpp @@ -0,0 +1,15 @@ +#pragma once +#include +#include +#include +#include + +/** + * @note a semilattice + */ +template +struct max_index_monoid { + typedef std::pair value_type; + value_type unit() const { return std::make_pair(std::numeric_limits::lowest(), INT_MIN); } + value_type mult(value_type a, value_type b) const { return std::max(a, b); } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/min.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/min.hpp new file mode 100644 index 0000000..4fcff30 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/min.hpp @@ -0,0 +1,10 @@ +#pragma once +#include +#include + +template +struct min_monoid { + typedef T value_type; + value_type unit() const { return std::numeric_limits::max(); } + value_type mult(value_type a, value_type b) const { return std::min(a, b); } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/min_count.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/min_count.hpp new file mode 100644 index 0000000..ae2fc6a --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/min_count.hpp @@ -0,0 +1,19 @@ +#pragma once +#include +#include + +template +struct min_count_monoid { + typedef std::pair value_type; + value_type unit() const { + return std::make_pair(std::numeric_limits::max(), 0); + } + value_type mult(value_type a, value_type b) const { + if (a.first < b.first) return a; + if (a.first > b.first) return b; + return std::make_pair(a.first, a.second + b.second); + } + static value_type make(T a) { + return std::make_pair(a, 1); + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/min_index.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/min_index.hpp new file mode 100644 index 0000000..b93e999 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/min_index.hpp @@ -0,0 +1,15 @@ +#pragma once +#include +#include +#include +#include + +/** + * @note a semilattice + */ +template +struct min_index_monoid { + typedef std::pair value_type; + value_type unit() const { return std::make_pair(std::numeric_limits::max(), INT_MAX); } + value_type mult(value_type a, value_type b) const { return std::min(a, b); } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/min_max.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/min_max.hpp new file mode 100644 index 0000000..3b81650 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/min_max.hpp @@ -0,0 +1,15 @@ +#pragma once +#include +#include +#include + +template +struct min_max_monoid { + typedef std::pair value_type; + value_type unit() const { + return std::make_pair(std::numeric_limits::max(), std::numeric_limits::lowest()); + } + value_type mult(value_type a, value_type b) const { + return std::make_pair(std::min(a.min, b.min), std::max(a.max, b.max)); + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/plus.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/plus.hpp new file mode 100644 index 0000000..420be62 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/plus.hpp @@ -0,0 +1,8 @@ +#pragma once + +template +struct plus_monoid { + typedef T value_type; + value_type unit() const { return value_type(); } + value_type mult(value_type a, value_type b) const { return a + b; } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/plus_count.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/plus_count.hpp new file mode 100644 index 0000000..ec644a2 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/plus_count.hpp @@ -0,0 +1,16 @@ +#pragma once +#include + +template +struct plus_count_monoid { + typedef std::pair value_type; + value_type unit() const { + return std::make_pair(T(), 0); + } + value_type mult(value_type a, value_type b) const { + return std::make_pair(a.first + b.first, a.second + b.second); + } + static value_type make(T a) { + return std::make_pair(a, 1); + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/plus_max_action.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/plus_max_action.hpp new file mode 100644 index 0000000..a4075f1 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/plus_max_action.hpp @@ -0,0 +1,13 @@ +#pragma once +#include "../monoids/plus.hpp" +#include "../monoids/max.hpp" + +/** + * @note lazy_propagation_segment_tree, plus_monoid, plus_max_action > is a starry sky tree + */ +template +struct plus_max_action { + typename max_monoid::value_type operator () (typename plus_monoid::value_type f, typename max_monoid::value_type x) const { + return (x == max_monoid().unit() ? x : f + x); + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/plus_min_action.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/plus_min_action.hpp new file mode 100644 index 0000000..ec5e03a --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/plus_min_action.hpp @@ -0,0 +1,10 @@ +#pragma once +#include "../monoids/plus.hpp" +#include "../monoids/min.hpp" + +template +struct plus_min_action { + typename min_monoid::value_type operator () (typename plus_monoid::value_type f, typename min_monoid::value_type x) const { + return (x == min_monoid().unit() ? x : f + x); + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/plus_min_count_action.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/plus_min_count_action.hpp new file mode 100644 index 0000000..e831fa0 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/plus_min_count_action.hpp @@ -0,0 +1,15 @@ +#pragma once +#include +#include "../monoids/plus.hpp" +#include "../monoids/min_count.hpp" + +/** + * @note lazy_propagation_segment_tree, plus_monoid, plus_min_count_action > can count a number of zeros (or, non-zero numbers) in the array + */ +template +struct plus_min_count_action { + typename min_count_monoid::value_type operator () (typename plus_monoid::value_type f, typename min_count_monoid::value_type x) const { + if (x.first == min_count_monoid().unit().first) return x; + return std::make_pair(f + x.first, x.second); + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/product.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/product.hpp new file mode 100644 index 0000000..32f265e --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/product.hpp @@ -0,0 +1,15 @@ +#pragma once +#include + +template +struct product_monoid { + typedef std::pair value_type; + Monoid1 mon1; + Monoid2 mon2; + value_type unit() const { + return std::make_pair(mon1.unit(), mon2.unit()); + } + value_type mult(value_type a, value_type b) const { + return std::make_pair(mon1.mult(a.first, b.first), mon2.mult(a.second, b.second)); + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/reversible.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/reversible.hpp new file mode 100644 index 0000000..29cf6fd --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/reversible.hpp @@ -0,0 +1,16 @@ +#pragma once +#include + +template +struct reversible_monoid { + typedef typename Monoid::value_type base_type; + typedef std::pair value_type; + Monoid base; + reversible_monoid() = default; + reversible_monoid(const Monoid & base_) : base(base_) {} + value_type unit() const { return std::make_pair(base.unit(), base.unit()); } + value_type mult(const value_type & a, const value_type & b) const { return std::make_pair(base.mult(a.first, b.first), base.mult(b.second, a.second)); } + static value_type make(const base_type & x) { return std::make_pair(x, x); } + static value_type reverse(const value_type & a) { return std::make_pair(a.second, a.first); } + static base_type get(const value_type & a) { return a.first; } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/trivial.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/trivial.hpp new file mode 100644 index 0000000..0ac46c3 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/trivial.hpp @@ -0,0 +1,7 @@ +#pragma once + +struct trivial_monoid { + typedef struct {} value_type; + value_type unit() const { return (value_type) {}; } + value_type mult(value_type a, value_type b) const { return (value_type) {}; } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/trivial_action.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/trivial_action.hpp new file mode 100644 index 0000000..44f2fc4 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/monoids/trivial_action.hpp @@ -0,0 +1,10 @@ +#pragma once +#include "../monoids/trivial.hpp" + +/** + * @note lazy_propagation_segment_tree > is the same to segment_tree + */ +template +struct trivial_action { + T operator () (typename trivial_monoid::value_type f, T x) const { return x; } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/order_of_calls.txt b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/order_of_calls.txt new file mode 100644 index 0000000..761b301 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/order_of_calls.txt @@ -0,0 +1,9 @@ +#in original code: +vertex_filtration_comp_cuda.cpp::vert_filt_persistence_batch +calls: + vertex_filtration_comp_cuda.cpp::vert_filt_persistence_single + calls: + vertex_filtration_comp_cuda.cpp::vert_filt_comp_calculate_persistence_args + calc_pers_cuda.cu::calculate_persistence + vr_comp_cuda.cu::calculate_persistence_output_to_barcode_tensors + diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/param_checks_cuda.cuh b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/param_checks_cuda.cuh new file mode 100644 index 0000000..93f3c66 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/param_checks_cuda.cuh @@ -0,0 +1,21 @@ +#pragma once + + +#include + + +#define CHECK_CUDA(x) AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) AT_ASSERTM(x.is_contiguous(), #x " must be contiguous") +#define CHECK_TENSOR_CUDA_CONTIGUOUS(x) \ + CHECK_CUDA(x); \ + CHECK_CONTIGUOUS(x) +#define CHECK_TENSOR_INT64(x) AT_ASSERTM(x.type().scalarType() == ScalarType::Long, "expected " #x "to be of scalar type int64") + +#define CHECK_SMALLER_EQ(x, y) AT_ASSERTM(x <= y, "expected " #x "<=" #y) +#define CHECK_EQUAL(x, y) AT_ASSERTM(x == y, "expected " #x "==" #y) +#define CHECK_GREATER_EQ(x, y) AT_ASSERTM(x >= y, "expected " #x ">=" #y) + +#define CHECK_SAME_DEVICE(x, y) AT_ASSERTM(x.device() == y.device(), #x, #y "are not on same device") + + +#define PRINT(x) std::cout << x << std::endl \ No newline at end of file diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/CMakeLists.txt b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/CMakeLists.txt new file mode 100644 index 0000000..c845d54 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/CMakeLists.txt @@ -0,0 +1,60 @@ +cmake_minimum_required(VERSION 2.8) + +project("Phat") + +set(CMAKE_BUILD_TYPE "Release") + +INCLUDE(CheckCXXSourceCompiles) + +include_directories (include) + +FIND_PACKAGE(OpenMP) + +set (CMAKE_REQUIRED_FLAGS ${OpenMP_CXX_FLAGS}) +CHECK_CXX_SOURCE_COMPILES(" +#include +int main() { +#if (_OPENMP >= 200805 || _MSC_VER >= 1500) + return 0; +#else + breaks_on_purpose +#endif +} +" OPENMP_VERSION) + +if(OPENMP_VERSION) + + + +set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}") +set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_EXE_LINKER_FLAGS}") +else() +message(" + +OpenMP 3.0 not supported by the compiler (${CMAKE_CXX_COMPILER})! +To get optimal performance for the \"chunk\" algorithm, use a C++ compiler with OpenMP 3.0 support (e.g., GCC >=4.4). +To use a different compiler, pass it to cmake in the variable CMAKE_CXX_COMPILER: + cmake . -DCMAKE_CXX_COMPILER=g++-4.7 + +") +endif() + +FILE(GLOB_RECURSE all_headers "include/phat/*.h") + +FILE(GLOB general_includes "include/phat/*.h") +FILE(GLOB algorithms_includes "include/phat/algorithms/*.h") +FILE(GLOB helpers_includes "include/phat/helpers/*.h") +FILE(GLOB representations_includes "include/phat/representations/*.h") + +add_executable (simple_example src/simple_example.cpp ${all_headers}) +add_executable (relative_example src/relative_example.cpp ${all_headers}) +add_executable (self_test src/self_test.cpp ${all_headers}) +add_executable (phat src/phat.cpp ${all_headers}) +add_executable (info src/info.cpp ${all_headers}) +add_executable (benchmark src/benchmark.cpp ${all_headers}) +add_executable (convert src/convert.cpp ${all_headers}) + +source_group(Header\ Files FILES ${general_includes}) +source_group(Header\ Files\\helpers FILES ${helpers_includes}) +source_group(Header\ Files\\representations FILES ${representations_includes}) +source_group(Header\ Files\\algorithms FILES ${algorithms_includes}) diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/COPYING b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/COPYING new file mode 100644 index 0000000..94a9ed0 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/COPYING @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If the Library as you +received it does not specify a version number of the GNU Lesser +General Public License, you may choose any version of the GNU Lesser +General Public License ever published by the Free Software Foundation. + + If the Library as you received it specifies that a proxy can decide +whether future versions of the GNU Lesser General Public License shall +apply, that proxy's public statement of acceptance of any version is +permanent authorization for you to choose that version for the +Library. diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/INSTALL b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/INSTALL new file mode 100644 index 0000000..34ee9b6 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/INSTALL @@ -0,0 +1 @@ +To create [GNU Makefiles | Visual Studio Projects] you can run CMake (available at http://www.cmake.org/) in this directory. \ No newline at end of file diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/README.md b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/README.md new file mode 100644 index 0000000..6a2172f --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/README.md @@ -0,0 +1,136 @@ +# PHAT (Persistent Homology Algorithm Toolbox), v1.4.0 +Copyright 2013, 2014 IST Austria + +###### Project Founders + +Ulrich Bauer, Michael Kerber, Jan Reininghaus + +###### Contributors + +Hubert Wagner, Primoz Skraba + +###### Downloads + * [https://drive.google.com/uc?id=0B7Yz6TPEpiGEMGFNQ3FPX3ltelk&export=download PHAT, v1.3.0] + * [https://drive.google.com/uc?id=0B7Yz6TPEpiGENE9KUnhUSFdFQUk&export=download PHAT, v1.2.1] + * [https://drive.google.com/uc?id=0B7Yz6TPEpiGERGZFbjlXaUt1ZWM&export=download benchmark data] + +##### Description + +This software library contains methods for computing the persistence pairs of a +filtered cell complex represented by an ordered boundary matrix with Z2 coefficients. +For an introduction to persistent homology, see the textbook `[1]`. This software package +contains code for several algorithmic variants: + + * The "standard" algorithm (see `[1]`, p.153) + * The "row" algorithm from `[2]` (called pHrow in there) + * The "twist" algorithm, as described in `[3]` (default algorithm) + * The "chunk" algorithm presented in `[4]` + +The last two algorithms exploit the special structure of the boundary matrix +to take shortcuts in the computation. The chunk algorithm makes use of multiple +CPU cores if it is compiled with OpenMP support. + +All algorithms are implemented as functor classes that manipulate a given `boundary_matrix` (to be defined below) object to reduced form. +From this reduced form one can then easily extract the persistence pairs. +Alternatively, one can use the `compute_persistence_pairs function` which takes an algorithm as a template parameter, reduces the given `boundary_matrix` and stores the resulting pairs in a given `persistence_pairs` object. + +The `boundary_matrix` class takes a "Representation" class as template parameter. This representation defines +how columns of the matrix are represented and how low-level operations +(e.g., column additions) are performed. The right choice of the representation +class can be as important for the performance of the program as choosing the +algorithm. We provide the following choices of representation classes: + + * `vector_vector`: Each column is represented as a sorted `std::vector` of integers, containing the indices of the non-zero entries of the column. The matrix itself is a `std::vector` of such columns. + * `vector_heap`: Each column is represented as a heapified `std::vector` of integers, containing the indices of the non-zero entries of the column. The matrix itself is a `std::vector` of such columns. + * `vector_set`: Each column is a `std::set` of integers, with the same meaning as above. The matrix is stored as a `std::vector` of such columns. + * `vector_list`: Each column is a sorted `std::list` of integers, with the same meaning as above. The matrix is stored as a `std::vector` of such columns. + * `sparse_pivot_column`: The matrix is stored as in the vector_vector representation. However, when a column is manipulated, it is first converted into a `std::set`, using an extra data field called the "pivot column". When another column is manipulated later, the pivot column is converted back to the `std::vector` representation. This can lead to significant speed improvements when many columns are added to a given pivot column consecutively. In a multicore setup, there is one pivot column per thread. + * `heap_pivot_column`: The same idea as in the sparse version. Instead of a `std::set`, the pivot column is represented by a `std::priority_queue`. + * `full_pivot_column`: The same idea as in the sparse version. However, instead of a `std::set`, the pivot column is expanded into a bit vector of size n (the dimension of the matrix). To avoid costly initializations, the class remembers which entries have been manipulated for a pivot column and updates only those entries when another column becomes the pivot. + * `bit_tree_pivot_column` (default representation): Similar to the `full_pivot_column` but the implementation is more efficient. Internally it is a bit-set with fast iteration over nonzero elements, and fast access to the maximal element. + +There are two ways to interface with the library: + + * using files: + write the boundary matrix / filtration into a file "input" (see below for the file format). + compile `src/phat.cpp` and run it: + ``` + phat [--ascii] input output + ``` + read the resulting persistence pairs into your program + + * using the C++ library interface: + include all headers found in `src/phat.cpp` + define a boundary matrix object, e.g. +`phat::boundary_matrix< bit_tree_pivot_column > boundary_matrix;` + set the number of columns: +`boundary_matrix.set_num_cols(...);` + initialize each column using + +``` +boundary_matrix.set_col(...); +boundary_matrix.set_dim(...); +``` + + +Define an object to hold the result: + +`phat::persistence_pairs pairs;` + + +Run an algorithm like this: + +`phat::compute_persistence_pairs< phat::twist_reduction >( pairs, boundary_matrix );` + + +Examine the result: + +`pairs.get_num_pairs()` +`pairs.get_pair(...)` + + +A simple example that demonstrates this functionality can be found in `src/simple_example.cpp`; another example, demonstrating how to compute persistent relative homology, can be found in `src/relative_example.cpp`. + +##### File Formats + +The library supports input and output in ascii and binary format +through the methods `[load|save]_[ascii|binary]` in the classes `boundary_matrix` +and `persistence_pairs`. The file formats are defined as follows: + +* `boundary_matrix` - ascii: + The file represents the filtration of the cell complex, containing one cell + per line (empty lines and lines starting with "#" are ignored). A cell is given by + a sequence of integers, separated by spaces, where the first integer denotes the + dimension of the cell, and all following integers give the indices + of the cells that form its boundary (the index of a cell is its position + in the filtration, starting with 0). + A sample file `single_triangle.dat` can be found in the examples folder. + +* `boundary_matrix` - binary: + In binary format, the file is simply interpreted as a sequence of 64 bit signed integer + numbers. The first number is interpreted as the number of cells of the complex. The + descriptions of the cells is expected to follow, with the first number representing the + dimension of the cell, the next number, say N, representing the size of the boundary, + followed by N numbers denoting the indices of the boundary cells. + A sample file `single_triangle.bin` can be found in the examples folder. + +* `persistence_pairs` - ascii: + The file contains the persistence pairs, sorted by birth index. The first integer in the + file is equal to the number of pairs. It is followed by pairs of integers encode the + respective birth and death indices. + A sample file `single_triangle_persistence_pairs.dat` can be found in the examples folder. + +* `persistence_pairs` - binary: + Same as ascii format, see above. Only now the integers are encoded as 64bit signed integers. + A sample file `single_triangle_persistence_pairs.bin` can be found in the examples folder. + +##### Supported Platforms + * Visual Studio 2008 and 2012 (2010 untested) + * GCC version 4.4. and higher + +##### References + + 1. H.Edelsbrunner, J.Harer: _Computational Topology, An Introduction_. American Mathematical Society, 2010, ISBN 0-8218-4925-5 + 2. V.de Silva, D.Morozov, M.Vejdemo-Johansson: _Dualities in persistent (co)homology_. Inverse Problems 27, 2011 + 3. C.Chen, M.Kerber: _Persistent Homology Computation With a Twist_. 27th European Workshop on Computational Geometry, 2011. + 4. U.Bauer, M.Kerber, J.Reininghaus: _Clear and Compress: Computing Persistent Homology in Chunks_. [http://arxiv.org/pdf/1303.0477.pdf arXiv:1303.0477] diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/addons/alpha_3.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/addons/alpha_3.cpp new file mode 100644 index 0000000..5ce855a --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/addons/alpha_3.cpp @@ -0,0 +1,448 @@ +#include + +#include +#include +#include + +#include +#include + +#include +#include +#include +#include + +template +struct Vertex_info_3 { + + typedef Index_ Index; + + Vertex_info_3() { + index_=boost::none; + } + + bool has_index() { + return index_; + } + + Index index() { + CGAL_assertion(has_index()); + return index_.get(); + } + + void set_index(Index I) { + index_=I; + } + +private: + boost::optional index_; +}; + + +template +struct Cell_info_3 { + + typedef Index_ Index; + typedef FT_ FT; + + Cell_info_3() { + for(std::size_t i=0; i<6; i++) { + edge_index_[i] = boost::none; + } + for(std::size_t i=0; i<4; i++) { + facet_index_[i] = boost::none; + } + } + + int edge_conv(int i, int j) { + if(i>j) std::swap(i,j); + if(i==0 && j==1) return 0; + if(i==0 && j==2) return 1; + if(i==0 && j==3) return 2; + if(i==1 && j==2) return 3; + if(i==1 && j==3) return 4; + if(i==2 && j==3) return 5; + } + + + bool has_edge_index(int i, int j) { + return edge_index_[edge_conv(i,j)]; + } + + Index edge_index(int i, int j) { + CGAL_assertion(has_edge_index(i,j)); + int k = edge_conv(i,j); + return edge_index_[k].get(); + } + + bool has_facet_index(int i) { + CGAL_assertion(i>=0 && i<4); + return facet_index_[i]; + } + + Index facet_index(int i) { + CGAL_assertion(has_facet_index(i)); + return facet_index_[i].get(); + } + + void set_edge_index(int i, int j, Index I) { + edge_index_[edge_conv(i,j)]=I; + } + + void set_facet_index(int i, Index I) { + facet_index_[i]=I; + } + + FT filtration_value; + FT filtration_value_of_facet[4]; + +private: + + + + boost::optional edge_index_[6]; + boost::optional facet_index_[4]; + +}; + +typedef phat::dimension Dim; +typedef std::vector Dim_container; +typedef phat::index Index; +typedef phat::column Column; +typedef std::vector > Matrix; + +typedef CGAL::Exact_predicates_exact_constructions_kernel Gt; +typedef Gt::FT FT; + +typedef CGAL::Triangulation_cell_base_with_info_3,Gt> Cb; +typedef CGAL::Triangulation_vertex_base_with_info_3,Gt> Vb; +//typedef CGAL::Triangulation_hierarchy_vertex_base_3 Vbh; +typedef CGAL::Triangulation_data_structure_3 Tds; +typedef CGAL::Delaunay_triangulation_3 DT_3; +typedef Gt::Point_3 Point; + + +typedef DT_3::Vertex_handle Vertex_handle; +typedef DT_3::Edge Edge; +typedef DT_3::Facet Facet; +typedef DT_3::Cell_handle Cell_handle; + +typedef DT_3::Cell_circulator Cell_circulator; +typedef DT_3::Facet_circulator Facet_circulator; + + +void set_index_of_edge(const DT_3& dt, const Edge& e, Index I) { + + Vertex_handle v1 = e.first->vertex(e.second); + Vertex_handle v2 = e.first->vertex(e.third); + + Cell_circulator ch=dt.incident_cells(e); + Cell_circulator ch_start=ch; + int count=0; + do { + ch->info().set_edge_index(ch->index(v1),ch->index(v2),I); + ch++; + count++; + } while(ch!=ch_start); + //std::cout << "Did " << count << " updates" << std::endl; +} + +void set_index_of_facet(const DT_3& dt, const Facet& f, Index I) { + + f.first->info().set_facet_index(f.second,I); + Facet mf = dt.mirror_facet(f); + mf.first->info().set_facet_index(mf.second,I); + +} + +template +struct Sort_triples { + + bool operator() (const Triple& a, const Triple& b) { + if(a.first < b.first) return true; + if(a.first > b.first) return false; + return a.second < b.second; + } + +}; + +// Returns whether the facet facet is attached to the cell +// that is used to represent facet +bool edge_attached_to(const DT_3& dt, const Edge& edge, const Facet& facet) { + + if(dt.is_infinite(facet)) { + return false; + } + + Vertex_handle v1 = edge.first->vertex(edge.second); + Vertex_handle v2 = edge.first->vertex(edge.third); + + Cell_handle cell = facet.first; + int i1 = facet.second; + int i2 = cell->index(v1); + int i3 = cell->index(v2); + CGAL_assertion(i1!=i2); + CGAL_assertion(i1!=i3); + CGAL_assertion(i2!=i3); + int j = 0; + + while(j==i1 || j==i2 || j==i3) { + j++; + } + // j is the index of the third point of the facet + Vertex_handle w = cell->vertex(j); + + return CGAL::side_of_bounded_sphere(v1->point(),v2->point(),w->point())==CGAL::ON_BOUNDED_SIDE; + +} + + +// Returns whether the facet facet is attached to the cell +// that is used to represent facet +bool triangle_attached_to(const DT_3& dt, const Facet& facet) { + Cell_handle cell = facet.first; + int index = facet.second; + + if(dt.is_infinite(cell)) { + return false; + } + + Vertex_handle v1 = cell->vertex((index+1)%4); + Vertex_handle v2 = cell->vertex((index+2)%4); + Vertex_handle v3 = cell->vertex((index+3)%4); + + Vertex_handle w = cell->vertex(facet.second); + + return CGAL::side_of_bounded_sphere(v1->point(),v2->point(),v3->point(),w->point())==CGAL::ON_BOUNDED_SIDE; +} + + + +int main(int argc, char** argv) +{ + + if(argc<2) { + std::cerr << "Need input file" << std::endl; + std::exit(1); + } + + std::list lp; + Point p; + + //read input + std::ifstream is(argv[1]); + std::string next_line; + while( getline( is, next_line ) ) { + if( next_line != "" && next_line[ 0 ] != '#' ) { + //std::cerr << next_line; + std::stringstream sstr(next_line); + sstr >> p; + lp.push_back(p); + } + } + + std::cerr << "Read " << lp.size() << " points" << std::endl; + + std::cerr << "Compute Delaunay triangulation..." << std::endl; + DT_3 dt(lp.begin(),lp.end()); +// for (std::list::iterator it = lp.begin(); it != lp.end(); ++it) { +// dt.insert(*it); +// }/Users/uli/Downloads/neptune-raw.off/782_neptune-raw.pts.txt + + std::cerr << "Compute circumradii..." << std::endl; + + typedef CGAL::Triple Triple; + + std::vector circumradii; + + for (DT_3::Finite_cells_iterator cit = dt.finite_cells_begin(); + cit != dt.finite_cells_end(); cit++) { + Cell_handle ch(cit); + Vertex_handle v1 = cit->vertex(0); + Vertex_handle v2 = cit->vertex(1); + Vertex_handle v3 = cit->vertex(2); + Vertex_handle v4 = cit->vertex(3); + FT circumradius = CGAL::squared_radius(v1->point(),v2->point(),v3->point(),v4->point()); + circumradii.push_back(CGAL::make_triple(circumradius,3,CGAL::make_object(ch))); + ch->info().filtration_value = circumradius; + } + + for (DT_3::Finite_facets_iterator f = dt.finite_facets_begin(); + f != dt.finite_facets_end(); f++) { + + // Check Gabrielness (attachment) of the triangle to its incident tetrahedra + bool is_attached = false; + FT filtration_value; + + { + Cell_handle cell = f->first; + int index = f->second; + if( triangle_attached_to(dt,*f) ) { + filtration_value = cell->info().filtration_value; + is_attached=true; + } + } + + { + if(! is_attached) { + Facet g = dt.mirror_facet(*f); + Cell_handle cell = g.first; + int index = g.second; + if( triangle_attached_to(dt,g) ) { + filtration_value = cell->info().filtration_value; + is_attached=true; + } + } + } + if(! is_attached) { + Cell_handle cell = f->first; + int index = f->second; + filtration_value = CGAL::squared_radius(cell->vertex((index+1)%4)->point(), + cell->vertex((index+2)%4)->point(), + cell->vertex((index+3)%4)->point()); + } + circumradii.push_back(CGAL::make_triple(filtration_value,2,CGAL::make_object(*f))); + // Store filtration values in info object for further use + f->first->info().filtration_value_of_facet[f->second] = filtration_value; + Facet g = dt.mirror_facet(*f); + g.first->info().filtration_value_of_facet[g.second] = filtration_value; + } + + + for (DT_3::Finite_edges_iterator edge = dt.finite_edges_begin(); + edge != dt.finite_edges_end(); edge++) { + + // Check Gabrielness of the edge + bool is_attached = false; + FT filtration_value; + + Facet_circulator f_start = dt.incident_facets(*edge); + Facet_circulator f_it = f_start; + f_it++; + while( f_it!=f_start ) { + if(edge_attached_to(dt,*edge,*f_it)) { + Cell_handle cell = f_it->first; + int index = f_it->second; + FT new_filtration_value = cell->info().filtration_value_of_facet[index]; + // Now, maybe the edge is already attached to another triangle (can this really happen?) + if(is_attached) { + filtration_value = CGAL::min(filtration_value,new_filtration_value); + } else { + filtration_value = new_filtration_value; + } + is_attached=true; + } + f_it++; + } + + + if(! is_attached) { + Vertex_handle v1 = edge->first->vertex(edge->second); + Vertex_handle v2 = edge->first->vertex(edge->third); + filtration_value = CGAL::squared_radius(v1->point(),v2->point()); + } + + circumradii.push_back(CGAL::make_triple(filtration_value,1,CGAL::make_object(*edge))); + } + + + + + for (DT_3::Finite_vertices_iterator vertex = dt.finite_vertices_begin(); + vertex != dt.finite_vertices_end(); vertex++) { + Vertex_handle vh(vertex); + circumradii.push_back(CGAL::make_triple(FT(0),0,CGAL::make_object(vh))); + } + + + std::cerr << "Sort circumradii..." << std::endl; + + std::sort(circumradii.begin(),circumradii.end(),Sort_triples()); + + std::cerr << "Filtration of size " << circumradii.size() << std::endl; + + phat::boundary_matrix< phat::vector_vector > boundary_matrix; + + Vertex_handle v; + Edge e; + Facet f; + Cell_handle c; + + std::size_t filtration_index = 0; + std::size_t filtration_size = circumradii.size(); + + boundary_matrix.set_num_cols( filtration_size ); + + Index curr_index = 0; + + for(std::vector::const_iterator it = circumradii.begin(); + it != circumradii.end(); it++) { + + if(filtration_index % 100000 == 0) { + std::cerr << filtration_index << " of " << filtration_size + << std::endl; + } + filtration_index++; + + const CGAL::Object& obj = it->third; + + Column col; + + if(CGAL::assign(v,obj)) { + //std::cout << "Vertex " << it->second << std::endl; + boundary_matrix.set_dim(curr_index, 0); + v->info().set_index(curr_index); + boundary_matrix.set_col(curr_index, col); + } + if(CGAL::assign(e,obj)) { + //std::cout << "Edge " << it->second << std::endl; + boundary_matrix.set_dim(curr_index, 1); + Vertex_handle v1 = e.first->vertex(e.second); + CGAL_assertion(v1->info().has_index()); + Vertex_handle v2 = e.first->vertex(e.third); + CGAL_assertion(v2->info().has_index()); + Index i1 = v1->info().index(); + Index i2 = v2->info().index(); + if(i1>i2) { + std::swap(v1,v2); + std::swap(i1,i2); + } + col.push_back(i1); + col.push_back(i2); + boundary_matrix.set_col(curr_index, col); + set_index_of_edge(dt,e,curr_index); + } + if(CGAL::assign(f,obj)) { + //std::cout << "Facet " << it->second << std::endl; + boundary_matrix.set_dim(curr_index, 2); + + Index i1= f.first->info().edge_index( (f.second+1)%4, (f.second+2)%4 ); + col.push_back(i1); + Index i2= f.first->info().edge_index( (f.second+1)%4, (f.second+3)%4 ); + col.push_back(i2); + Index i3= f.first->info().edge_index( (f.second+2)%4, (f.second+3)%4 ); + col.push_back(i3); + std::sort(col.begin(),col.end()); + boundary_matrix.set_col(curr_index, col); + set_index_of_facet(dt,f,curr_index); + + } + if(CGAL::assign(c,obj)) { + //std::cout << "Cell " << it->second << std::endl; + boundary_matrix.set_dim(curr_index, 3); + col.push_back(c->info().facet_index(0)); + col.push_back(c->info().facet_index(1)); + col.push_back(c->info().facet_index(2)); + col.push_back(c->info().facet_index(3)); + std::sort(col.begin(),col.end()); + boundary_matrix.set_col(curr_index, col); + } + curr_index++; + } + + boundary_matrix.save_ascii("alpha_filtration.bin"); + + //phat::write(std::cout,M,dim_container); + + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/addons/rips.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/addons/rips.cpp new file mode 100644 index 0000000..e562443 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/addons/rips.cpp @@ -0,0 +1,91 @@ +#include +#include + +#include +#include + +#include // for BackInsertFunctor + +#include + +#include + +typedef PairwiseDistances Pair_distances; +typedef Pair_distances::DistanceType Distance_type; +typedef Pair_distances::IndexType Vertex; + +typedef Rips Generator; +typedef Generator::Simplex Smplx; +typedef Filtration Fltr; + +int main(int argc, char* argv[]) +{ + Dimension skeleton; + Distance_type max_distance; + std::string infilename; + + if(argc<2) { + std::cerr << "Requires inputfile" << std::endl; + std::exit(1); + } + infilename = argv[1]; + + if(argc>=3) { + skeleton=atoi(argv[2]); + if(skeleton==0) { + std::cerr << "# Command line argument 0 ignored" << std::endl; + skeleton=std::numeric_limits::max(); + } + } else { + skeleton=std::numeric_limits::max(); + } + + if(argc>=4) { + max_distance=atof(argv[3]); + } else { + max_distance=std::numeric_limits::max(); + } + + PointContainer points; + read_points(infilename, points); + + Pair_distances distances(points); + Generator rips(distances); + Generator::Evaluator size(distances); + Fltr f; + + // Generate 2-skeleton of the Rips complex for epsilon = 50 + rips.generate(skeleton, max_distance, make_push_back_functor(f)); + std::cerr << "# Generated complex of size: " << f.size() << std::endl; + + // Generate filtration with respect to distance and compute its persistence + f.sort(Generator::Comparison(distances)); + + + std::map simplex_map; + phat::index size_of_simplex_map=0; + + phat::boundary_matrix< phat::vector_vector > boundary_matrix; + boundary_matrix.set_num_cols( f.size() ); + + for(Fltr::Index it=f.begin();it!=f.end();it++) { + phat::column boundary_indices; + const Smplx& c = f.simplex(it); + for(Smplx::BoundaryIterator bit = c.boundary_begin(); bit != c.boundary_end(); bit++) + boundary_indices.push_back( simplex_map[*bit] ); + std::sort(boundary_indices.begin(),boundary_indices.end()); + + boundary_matrix.set_col( size_of_simplex_map, boundary_indices ); + + phat::dimension dim_of_column = boundary_indices.size()==0 ? 0 : boundary_indices.size()-1; + + boundary_matrix.set_dim( size_of_simplex_map, dim_of_column ); + + simplex_map[c] = size_of_simplex_map++; + } + + boundary_matrix.save_binary( "rips.bin" ); + + return 1; + +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/examples/single_triangle.bin b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/examples/single_triangle.bin new file mode 100644 index 0000000000000000000000000000000000000000..8d5c8fbb8ddd3bda4f137c7f4b16e368d1ee18d9 GIT binary patch literal 192 ocmZQ)AP+D?wK1V-hl#^zba@ybT^^keQ^yQ7A4ao4`K(YH01SWtC;$Ke literal 0 HcmV?d00001 diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/examples/single_triangle.dat b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/examples/single_triangle.dat new file mode 100644 index 0000000..928ee9b --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/examples/single_triangle.dat @@ -0,0 +1,12 @@ +# A simple matrix +0 +0 +0 +1 0 1 +1 0 2 +1 1 2 +# Here is the triangle +2 3 4 5 + + + diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/examples/single_triangle_persistence_pairs.bin b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/examples/single_triangle_persistence_pairs.bin new file mode 100644 index 0000000000000000000000000000000000000000..fe864ace91ac84038f19565665d99f2b5779c906 GIT binary patch literal 56 acmZQ(fB;4)%?zcPpfn4VW`)vhP#ORNr~nxN literal 0 HcmV?d00001 diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/examples/single_triangle_persistence_pairs.dat b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/examples/single_triangle_persistence_pairs.dat new file mode 100644 index 0000000..daaeef5 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/examples/single_triangle_persistence_pairs.dat @@ -0,0 +1,4 @@ +3 +1 3 +2 4 +5 6 diff --git 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z^YYD9j;U{L3?KFGsMippIzv)***foQm<>iPZ>!`F9W1~7_Y3*8GkkmWJjc~)KXd-xus5!|=GCYl?G4`$?Ypa>eX!^<-$41DB3%dh zbu@g;$Gdp|*27qj?QHm%mtU*-VRV_VqV`QbBV8Bybv1nS^WAufaW3l`-3%Y=AGGV} zG9T+0jDN3{Uw6aDc%(?{#yNkF==lT27d;H$x|1V*Gk!sr`HD5JVI0y^e!UD|TYU$j z`&m)WGT&>O|KF(oTOY$$SU-M$-NqQVpv!!92ZXM_bbaO5&+xI2oH&#Dh#Sna^f!Dw zXGdH?m-&t<{t!>Dm)`)x$GX~rbB>>W|8r)HP-m(<4>WvrZ;kxrJCNuy-z6F+&63}Z z@*8CMj;Vj}w26P)HQt4$cgLF5`Z;0XJJD=3!oWJbb zralgJe2wC|)N^#1Z-&y1dOJ*hw;De7Q@HkxekJ87e)E%Mb(!z+=i>Yb z`Hh#~1mnB0eB>MZQKzXtm3@oV-#DQApDx{<=Dx`n_x)PZo47T}@C``$fiCm$y;}0& zF8NJ90iWyl5*KK%ry4%mb;=jI%t!l8Jov5rrWroC*!Pjk&19v2E&YDF;bT7~_kk|+ zu`jZ=eD2Y4hK^Bt|Nfr4CK?Y;trO)f^DSKw^?Z8YC|@%T-|F7+`)-A0b(!y4oxewZ z_sMUT;bUCm(yx_%M|#XQe55nu8+4hkwSG^!J)r-W>X_4y`r-J`_i{{h+|9o7H< literal 0 HcmV?d00001 diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/algorithms/chunk_reduction.h b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/algorithms/chunk_reduction.h new file mode 100644 index 0000000..1797023 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/algorithms/chunk_reduction.h @@ -0,0 +1,223 @@ +/* Copyright 2013 IST Austria + Contributed by: Ulrich Bauer, Michael Kerber, Jan Reininghaus + + This file is part of PHAT. + + PHAT is free software: you can redistribute it and/or modify + it under the terms of the GNU Lesser General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + PHAT is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Lesser General Public License for more details. + + You should have received a copy of the GNU Lesser General Public License + along with PHAT. If not, see . */ + +#pragma once + +#include +#include + +namespace phat { + class chunk_reduction { + public: + enum column_type { GLOBAL + , LOCAL_POSITIVE + , LOCAL_NEGATIVE }; + + public: + template< typename Representation > + void operator() ( boundary_matrix< Representation >& boundary_matrix ) { + + + const index nr_columns = boundary_matrix.get_num_cols(); + if( omp_get_max_threads( ) > nr_columns ) + omp_set_num_threads( 1 ); + + const dimension max_dim = boundary_matrix.get_max_dim(); + + std::vector< index > lowest_one_lookup( nr_columns, -1 ); + std::vector < column_type > column_type( nr_columns, GLOBAL ); + std::vector< char > is_active( nr_columns, false ); + + const index chunk_size = omp_get_max_threads() == 1 ? (index)sqrt( (double)nr_columns ) : nr_columns / omp_get_max_threads(); + + std::vector< index > chunk_boundaries; + for( index cur_boundary = 0; cur_boundary < nr_columns; cur_boundary += chunk_size ) + chunk_boundaries.push_back( cur_boundary ); + chunk_boundaries.push_back( nr_columns ); + + for( dimension cur_dim = max_dim; cur_dim >= 1; cur_dim-- ) { + // Phase 1: Reduce chunks locally -- 1st pass + #pragma omp parallel for schedule( guided, 1 ) + for( index chunk_id = 0; chunk_id < (index)chunk_boundaries.size() - 1; chunk_id++ ) + _local_chunk_reduction( boundary_matrix, lowest_one_lookup, column_type, cur_dim, + chunk_boundaries[ chunk_id ], chunk_boundaries[ chunk_id + 1 ], chunk_boundaries[ chunk_id ] ); + boundary_matrix.sync(); + + // Phase 1: Reduce chunks locally -- 2nd pass + #pragma omp parallel for schedule( guided, 1 ) + for( index chunk_id = 1; chunk_id < (index)chunk_boundaries.size( ) - 1; chunk_id++ ) + _local_chunk_reduction( boundary_matrix, lowest_one_lookup, column_type, cur_dim, + chunk_boundaries[ chunk_id ], chunk_boundaries[ chunk_id + 1 ], chunk_boundaries[ chunk_id - 1 ] ); + boundary_matrix.sync( ); + } + + // get global columns + std::vector< index > global_columns; + for( index cur_col_idx = 0; cur_col_idx < nr_columns; cur_col_idx++ ) + if( column_type[ cur_col_idx ] == GLOBAL ) + global_columns.push_back( cur_col_idx ); + + // get active columns + #pragma omp parallel for + for( index idx = 0; idx < (index)global_columns.size(); idx++ ) + is_active[ global_columns[ idx ] ] = true; + _get_active_columns( boundary_matrix, lowest_one_lookup, column_type, global_columns, is_active ); + + // Phase 2+3: Simplify columns and reduce them + for( dimension cur_dim = max_dim; cur_dim >= 1; cur_dim-- ) { + // Phase 2: Simplify columns + std::vector< index > temp_col; + #pragma omp parallel for schedule( guided, 1 ), private( temp_col ) + for( index idx = 0; idx < (index)global_columns.size(); idx++ ) + if( boundary_matrix.get_dim( global_columns[ idx ] ) == cur_dim ) + _global_column_simplification( global_columns[ idx ], boundary_matrix, lowest_one_lookup, column_type, is_active, temp_col ); + boundary_matrix.sync(); + + // Phase 3: Reduce columns + for( index idx = 0; idx < (index)global_columns.size(); idx++ ) { + index cur_col = global_columns[ idx ]; + if( boundary_matrix.get_dim( cur_col ) == cur_dim && column_type[ cur_col ] == GLOBAL ) { + index lowest_one = boundary_matrix.get_max_index( cur_col ); + while( lowest_one != -1 && lowest_one_lookup[ lowest_one ] != -1 ) { + boundary_matrix.add_to( lowest_one_lookup[ lowest_one ], cur_col ); + lowest_one = boundary_matrix.get_max_index( cur_col ); + } + if( lowest_one != -1 ) { + lowest_one_lookup[ lowest_one ] = cur_col; + boundary_matrix.clear( lowest_one ); + } + boundary_matrix.finalize( cur_col ); + } + } + } + + boundary_matrix.sync(); + } + + protected: + template< typename Representation > + void _local_chunk_reduction( boundary_matrix< Representation >& boundary_matrix + , std::vector& lowest_one_lookup + , std::vector< column_type >& column_type + , const dimension cur_dim + , const index chunk_begin + , const index chunk_end + , const index row_begin ) { + + for( index cur_col = chunk_begin; cur_col < chunk_end; cur_col++ ) { + if( column_type[ cur_col ] == GLOBAL && boundary_matrix.get_dim( cur_col ) == cur_dim ) { + index lowest_one = boundary_matrix.get_max_index( cur_col ); + while( lowest_one != -1 && lowest_one >= row_begin && lowest_one_lookup[ lowest_one ] != -1 ) { + boundary_matrix.add_to( lowest_one_lookup[ lowest_one ], cur_col ); + lowest_one = boundary_matrix.get_max_index( cur_col ); + } + if( lowest_one >= row_begin ) { + lowest_one_lookup[ lowest_one ] = cur_col; + column_type[ cur_col ] = LOCAL_NEGATIVE; + column_type[ lowest_one ] = LOCAL_POSITIVE; + boundary_matrix.clear( lowest_one ); + boundary_matrix.finalize( cur_col ); + } + } + } + } + + template< typename Representation > + void _get_active_columns( const boundary_matrix< Representation >& boundary_matrix + , const std::vector< index >& lowest_one_lookup + , const std::vector< column_type >& column_type + , const std::vector< index >& global_columns + , std::vector< char >& is_active ) { + + const index nr_columns = boundary_matrix.get_num_cols(); + std::vector< char > finished( nr_columns, false ); + + std::vector< std::pair < index, index > > stack; + std::vector< index > cur_col_values; + #pragma omp parallel for schedule( guided, 1 ), private( stack, cur_col_values ) + for( index idx = 0; idx < (index)global_columns.size(); idx++ ) { + bool pop_next = false; + index start_col = global_columns[ idx ]; + stack.push_back( std::pair< index, index >( start_col, -1 ) ); + while( !stack.empty() ) { + index cur_col = stack.back().first; + index prev_col = stack.back().second; + if( pop_next ) { + stack.pop_back(); + pop_next = false; + if( prev_col != -1 ) { + if( is_active[ cur_col ] ) { + is_active[ prev_col ] = true; + } + if( prev_col == stack.back().first ) { + finished[ prev_col ] = true; + pop_next = true; + } + } + } else { + pop_next = true; + boundary_matrix.get_col( cur_col, cur_col_values ); + for( index idx = 0; idx < (index) cur_col_values.size(); idx++ ) { + index cur_row = cur_col_values[ idx ]; + if( ( column_type[ cur_row ] == GLOBAL ) ) { + is_active[ cur_col ] = true; + } else if( column_type[ cur_row ] == LOCAL_POSITIVE ) { + index next_col = lowest_one_lookup[ cur_row ]; + if( next_col != cur_col && !finished[ cur_col ] ) { + stack.push_back( std::make_pair( next_col, cur_col ) ); + pop_next = false; + } + } + } + } + } + } + } + + template< typename Representation > + void _global_column_simplification( const index col_idx + , boundary_matrix< Representation >& boundary_matrix + , const std::vector< index >& lowest_one_lookup + , const std::vector< column_type >& column_type + , const std::vector< char >& is_active + , std::vector< index >& temp_col ) + { + temp_col.clear(); + while( !boundary_matrix.is_empty( col_idx ) ) { + index cur_row = boundary_matrix.get_max_index( col_idx ); + switch( column_type[ cur_row ] ) { + case GLOBAL: + temp_col.push_back( cur_row ); + boundary_matrix.remove_max( col_idx ); + break; + case LOCAL_NEGATIVE: + boundary_matrix.remove_max( col_idx ); + break; + case LOCAL_POSITIVE: + if( is_active[ lowest_one_lookup[ cur_row ] ] ) + boundary_matrix.add_to( lowest_one_lookup[ cur_row ], col_idx ); + else + boundary_matrix.remove_max( col_idx ); + break; + } + } + std::reverse( temp_col.begin(), temp_col.end() ); + boundary_matrix.set_col( col_idx, temp_col ); + } + }; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/algorithms/row_reduction.h b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/algorithms/row_reduction.h new file mode 100644 index 0000000..cdd1a8f --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/algorithms/row_reduction.h @@ -0,0 +1,56 @@ +/* Copyright 2013 IST Austria + Contributed by: Ulrich Bauer, Michael Kerber, Jan Reininghaus + + This file is part of PHAT. + + PHAT is free software: you can redistribute it and/or modify + it under the terms of the GNU Lesser General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + PHAT is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Lesser General Public License for more details. + + You should have received a copy of the GNU Lesser General Public License + along with PHAT. If not, see . */ + +#pragma once + +#include +#include + +namespace phat { + class row_reduction { + public: + template< typename Representation > + void operator() ( boundary_matrix< Representation >& boundary_matrix ) { + + const index nr_columns = boundary_matrix.get_num_cols(); + std::vector< std::vector< index > > lowest_one_lookup( nr_columns ); + + for( index cur_col = nr_columns - 1; cur_col >= 0; cur_col-- ) { + if( !boundary_matrix.is_empty( cur_col ) ) + lowest_one_lookup[ boundary_matrix.get_max_index( cur_col ) ].push_back( cur_col ); + + if( !lowest_one_lookup[ cur_col ].empty() ) { + boundary_matrix.clear( cur_col ); + boundary_matrix.finalize( cur_col ); + std::vector< index >& cols_with_cur_lowest = lowest_one_lookup[ cur_col ]; + index source = *min_element( cols_with_cur_lowest.begin(), cols_with_cur_lowest.end() ); + for( index idx = 0; idx < (index)cols_with_cur_lowest.size(); idx++ ) { + index target = cols_with_cur_lowest[ idx ]; + if( target != source && !boundary_matrix.is_empty( target ) ) { + boundary_matrix.add_to( source, target ); + if( !boundary_matrix.is_empty( target ) ) { + index lowest_one_of_target = boundary_matrix.get_max_index( target ); + lowest_one_lookup[ lowest_one_of_target ].push_back( target ); + } + } + } + } + } + } + }; +} \ No newline at end of file diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/algorithms/spectral_sequence_reduction.h b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/algorithms/spectral_sequence_reduction.h new file mode 100644 index 0000000..bf442e6 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/algorithms/spectral_sequence_reduction.h @@ -0,0 +1,80 @@ +/* Copyright 2013 IST Austria + Contributed by: Jan Reininghaus + + This file is part of PHAT. + + PHAT is free software: you can redistribute it and/or modify + it under the terms of the GNU Lesser General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + PHAT is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Lesser General Public License for more details. + + You should have received a copy of the GNU Lesser General Public License + along with PHAT. If not, see . */ + +#pragma once + +#include +#include + +namespace phat { + class spectral_sequence_reduction { + public: + template< typename Representation > + void operator () ( boundary_matrix< Representation >& boundary_matrix ) { + + const index nr_columns = boundary_matrix.get_num_cols(); + std::vector< index > lowest_one_lookup( nr_columns, -1 ); + + //const index num_stripes = (index) sqrt( (double)nr_columns ); + const index num_stripes = omp_get_max_threads(); + + index block_size = ( nr_columns % num_stripes == 0 ) ? nr_columns / num_stripes : block_size = nr_columns / num_stripes + 1; + + std::vector< std::vector< index > > unreduced_cols_cur_pass( num_stripes ); + std::vector< std::vector< index > > unreduced_cols_next_pass( num_stripes ); + + for( index cur_dim = boundary_matrix.get_max_dim(); cur_dim >= 1 ; cur_dim-- ) { + #pragma omp parallel for schedule( guided, 1 ) + for( index cur_stripe = 0; cur_stripe < num_stripes; cur_stripe++ ) { + index col_begin = cur_stripe * block_size; + index col_end = std::min( (cur_stripe+1) * block_size, nr_columns ); + for( index cur_col = col_begin; cur_col < col_end; cur_col++ ) + if( boundary_matrix.get_dim( cur_col ) == cur_dim && boundary_matrix.get_max_index( cur_col ) != -1 ) + unreduced_cols_cur_pass[ cur_stripe ].push_back( cur_col ); + } + for( index cur_pass = 0; cur_pass < num_stripes; cur_pass++ ) { + boundary_matrix.sync(); + #pragma omp parallel for schedule( guided, 1 ) + for( int cur_stripe = 0; cur_stripe < num_stripes; cur_stripe++ ) { + index row_begin = (cur_stripe - cur_pass) * block_size; + index row_end = row_begin + block_size; + unreduced_cols_next_pass[ cur_stripe ].clear(); + for( index idx = 0; idx < (index)unreduced_cols_cur_pass[ cur_stripe ].size(); idx++ ) { + index cur_col = unreduced_cols_cur_pass[ cur_stripe ][ idx ]; + index lowest_one = boundary_matrix.get_max_index( cur_col ); + while( lowest_one != -1 && lowest_one >= row_begin && lowest_one < row_end && lowest_one_lookup[ lowest_one ] != -1 ) { + boundary_matrix.add_to( lowest_one_lookup[ lowest_one ], cur_col ); + lowest_one = boundary_matrix.get_max_index( cur_col ); + } + if( lowest_one != -1 ) { + if( lowest_one >= row_begin && lowest_one < row_end ) { + lowest_one_lookup[ lowest_one ] = cur_col; + boundary_matrix.clear( lowest_one ); + boundary_matrix.finalize( cur_col ); + } else { + unreduced_cols_next_pass[ cur_stripe ].push_back( cur_col ); + } + } + } + unreduced_cols_next_pass[ cur_stripe ].swap( unreduced_cols_cur_pass[ cur_stripe ] ); + } + } + } + } + }; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/algorithms/standard_reduction.h b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/algorithms/standard_reduction.h new file mode 100644 index 0000000..6357684 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/algorithms/standard_reduction.h @@ -0,0 +1,48 @@ +/* Copyright 2013 IST Austria + Contributed by: Ulrich Bauer, Michael Kerber, Jan Reininghaus + + This file is part of PHAT. + + PHAT is free software: you can redistribute it and/or modify + it under the terms of the GNU Lesser General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + PHAT is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Lesser General Public License for more details. + + You should have received a copy of the GNU Lesser General Public License + along with PHAT. If not, see . */ + +#pragma once + +#include +#include + +namespace phat { + class standard_reduction { + public: + template< typename Representation > + void operator() ( boundary_matrix< Representation >& boundary_matrix ) { + + const index nr_columns = boundary_matrix.get_num_cols(); + std::vector< index > lowest_one_lookup( nr_columns, -1 ); + + for( index cur_col = 0; cur_col < nr_columns; cur_col++ ) { + index lowest_one = boundary_matrix.get_max_index( cur_col ); + //std::cout<<"reducing col: "<. */ + +#pragma once + +#include +#include + +namespace phat { + class twist_reduction { + public: + template< typename Representation > + void operator () ( boundary_matrix< Representation >& boundary_matrix ) { + + const index nr_columns = boundary_matrix.get_num_cols(); + std::vector< index > lowest_one_lookup( nr_columns, -1 ); + std::cout<<"boundary_matrix.get_max_dim(): "<= 1 ; cur_dim-- ) { + for( index cur_dim = 2; cur_dim >= 1 ; cur_dim-- ) { + for( index cur_col = 0; cur_col < nr_columns; cur_col++ ) { + if( boundary_matrix.get_dim( cur_col ) == cur_dim ) { + std::cout<<"reducing col: "<. */ + +#pragma once + +#include +#include + +// interface class for the main data structure -- implementations of the interface can be found in ./representations +namespace phat { + template< class Representation = bit_tree_pivot_column > + class boundary_matrix + { + + protected: + Representation rep; + + // interface functions -- actual implementation and complexity depends on chosen @Representation template + public: + // get overall number of columns in boundary_matrix + index get_num_cols() const { return rep._get_num_cols(); } + + // set overall number of columns in boundary_matrix + void set_num_cols( index nr_of_columns ) { rep._set_num_cols( nr_of_columns ); } + + // get dimension of given index + dimension get_dim( index idx ) const { return rep._get_dim( idx ); } + + // set dimension of given index + void set_dim( index idx, dimension dim ) { rep._set_dim( idx, dim ); } + + // replaces content of @col with boundary of given index + void get_col( index idx, column& col ) const { col.clear(); rep._get_col( idx, col ); } + + // set column @idx to the values contained in @col + void set_col( index idx, const column& col ) { rep._set_col( idx, col ); } + + // true iff boundary of given column is empty + bool is_empty( index idx ) const { return rep._is_empty( idx ); } + + // largest index of given column (new name for lowestOne()) -- NOT thread-safe + index get_max_index( index idx ) const { return rep._get_max_index( idx ); } + + // removes maximal index from given column + void remove_max( index idx ) { rep._remove_max( idx ); } + + // adds column @source to column @target' + void add_to( index source, index target ) { rep._add_to( source, target ); } + + // clears given column + void clear( index idx ) { rep._clear( idx ); } + + // finalizes given column + void finalize( index idx ) { rep._finalize( idx ); } + + // syncronizes all internal data structures -- has to be called before and after any multithreaded access! + void sync() { rep._sync(); } + + // info functions -- independent of chosen 'Representation' + public: + // maximal dimension + dimension get_max_dim() const { + dimension cur_max_dim = 0; + for( index idx = 0; idx < get_num_cols(); idx++ ) + cur_max_dim = get_dim( idx ) > cur_max_dim ? get_dim( idx ) : cur_max_dim; + return cur_max_dim; + } + + // number of nonzero rows for given column @idx + index get_num_rows( index idx ) const { + column cur_col; + get_col( idx, cur_col ); + return cur_col.size(); + } + + // maximal number of nonzero rows of all columns + index get_max_col_entries() const { + index max_col_entries = -1; + const index nr_of_columns = get_num_cols(); + for( index idx = 0; idx < nr_of_columns; idx++ ) + max_col_entries = get_num_rows( idx ) > max_col_entries ? get_num_rows( idx ) : max_col_entries; + return max_col_entries; + } + + // maximal number of nonzero cols of all rows + index get_max_row_entries() const { + size_t max_row_entries = 0; + const index nr_of_columns = get_num_cols(); + std::vector< std::vector< index > > transposed_matrix( nr_of_columns ); + column temp_col; + for( index cur_col = 0; cur_col < nr_of_columns; cur_col++ ) { + get_col( cur_col, temp_col ); + for( index idx = 0; idx < (index)temp_col.size(); idx++) + transposed_matrix[ temp_col[ idx ] ].push_back( cur_col ); + } + for( index idx = 0; idx < nr_of_columns; idx++ ) + max_row_entries = transposed_matrix[ idx ].size() > max_row_entries ? transposed_matrix[ idx ].size() : max_row_entries; + return max_row_entries; + } + + // overall number of entries in the matrix + index get_num_entries() const { + index number_of_nonzero_entries = 0; + const index nr_of_columns = get_num_cols(); + for( index idx = 0; idx < nr_of_columns; idx++ ) + number_of_nonzero_entries += get_num_rows( idx ); + return number_of_nonzero_entries; + } + + // operators / constructors + public: + boundary_matrix() {}; + + template< class OtherRepresentation > + boundary_matrix( const boundary_matrix< OtherRepresentation >& other ) { + *this = other; + } + + template< typename OtherRepresentation > + bool operator==( const boundary_matrix< OtherRepresentation >& other_boundary_matrix ) const { + const index number_of_columns = this->get_num_cols(); + + if( number_of_columns != other_boundary_matrix.get_num_cols() ) + return false; + + column temp_col; + column other_temp_col; + for( index idx = 0; idx < number_of_columns; idx++ ) { + this->get_col( idx, temp_col ); + other_boundary_matrix.get_col( idx, other_temp_col ); + if( temp_col != other_temp_col || this->get_dim( idx ) != other_boundary_matrix.get_dim( idx ) ) + return false; + } + return true; + } + + template< typename OtherRepresentation > + bool operator!=( const boundary_matrix< OtherRepresentation >& other_boundary_matrix ) const { + return !( *this == other_boundary_matrix ); + } + + template< typename OtherRepresentation > + boundary_matrix< Representation >& operator=( const boundary_matrix< OtherRepresentation >& other ) + { + const index nr_of_columns = other.get_num_cols(); + this->set_num_cols( nr_of_columns ); + column temp_col; + for( index cur_col = 0; cur_col < nr_of_columns; cur_col++ ) { + this->set_dim( cur_col, other.get_dim( cur_col ) ); + other.get_col( cur_col, temp_col ); + this->set_col( cur_col, temp_col ); + } + + // by convention, always return *this + return *this; + } + + // I/O -- independent of chosen 'Representation' + public: + + // initializes boundary_matrix from (vector, vector) pair -- untested + template< typename index_type, typename dimemsion_type > + void load_vector_vector( const std::vector< std::vector< index_type > >& input_matrix, const std::vector< dimemsion_type >& input_dims ) { + const index nr_of_columns = (index)input_matrix.size(); + this->set_num_cols( nr_of_columns ); + column temp_col; + #pragma omp parallel for private( temp_col ) + for( index cur_col = 0; cur_col < nr_of_columns; cur_col++ ) { + this->set_dim( cur_col, (dimension)input_dims[ cur_col ] ); + + index num_rows = input_matrix[ cur_col ].size(); + temp_col.resize( num_rows ); + for( index cur_row = 0; cur_row < num_rows; cur_row++ ) + temp_col[ cur_row ] = (index)input_matrix[ cur_col ][ cur_row ]; + this->set_col( cur_col, temp_col ); + } + } + + template< typename index_type, typename dimemsion_type > + void save_vector_vector( std::vector< std::vector< index_type > >& output_matrix, std::vector< dimemsion_type >& output_dims ) { + const index nr_of_columns = get_num_cols(); + output_matrix.resize( nr_of_columns ); + output_dims.resize( nr_of_columns ); + column temp_col; + for( index cur_col = 0; cur_col < nr_of_columns; cur_col++ ) { + output_dims[ cur_col ] = (dimemsion_type)get_dim( cur_col ); + get_col( cur_col, temp_col ); + index num_rows = temp_col.size(); + output_matrix[ cur_col ].clear(); + output_matrix[ cur_col ].resize( num_rows ); + for( index cur_row = 0; cur_row < num_rows; cur_row++ ) + output_matrix[ cur_col ][ cur_row ] = (index_type)temp_col[ cur_row ]; + } + } + + + // Loads the boundary_matrix from given file in ascii format + // Format: each line represents a column, first number is dimension, other numbers are the content of the column. + // Ignores empty lines and lines starting with a '#'. + bool load_ascii( std::string filename ) { + // first count number of columns: + std::string cur_line; + std::ifstream dummy( filename .c_str() ); + if( dummy.fail() ) + return false; + + index number_of_columns = 0; + while( getline( dummy, cur_line ) ) { + cur_line.erase(cur_line.find_last_not_of(" \t\n\r\f\v") + 1); + if( cur_line != "" && cur_line[ 0 ] != '#' ) + number_of_columns++; + + } + this->set_num_cols( number_of_columns ); + dummy.close(); + + std::ifstream input_stream( filename.c_str() ); + if( input_stream.fail() ) + return false; + + column temp_col; + index cur_col = -1; + while( getline( input_stream, cur_line ) ) { + cur_line.erase(cur_line.find_last_not_of(" \t\n\r\f\v") + 1); + if( cur_line != "" && cur_line[ 0 ] != '#' ) { + cur_col++; + std::stringstream ss( cur_line ); + + int64_t temp_dim; + ss >> temp_dim; + this->set_dim( cur_col, (dimension) temp_dim ); + + int64_t temp_index; + temp_col.clear(); + while( ss.good() ) { + ss >> temp_index; + temp_col.push_back( (index)temp_index ); + } + std::sort( temp_col.begin(), temp_col.end() ); + this->set_col( cur_col, temp_col ); + } + } + + input_stream.close(); + return true; + } + + // Saves the boundary_matrix to given file in ascii format + // Format: each line represents a column, first number is dimension, other numbers are the content of the column + bool save_ascii( std::string filename ) { + std::ofstream output_stream( filename.c_str() ); + if( output_stream.fail() ) + return false; + + const index nr_columns = this->get_num_cols(); + column tempCol; + for( index cur_col = 0; cur_col < nr_columns; cur_col++ ) { + output_stream << (int64_t)this->get_dim( cur_col ); + this->get_col( cur_col, tempCol ); + for( index cur_row_idx = 0; cur_row_idx < (index)tempCol.size(); cur_row_idx++ ) + output_stream << " " << tempCol[ cur_row_idx ]; + output_stream << std::endl; + } + + output_stream.close(); + return true; + } + + // Loads boundary_matrix from given file + // Format: nr_columns % dim1 % N1 % row1 row2 % ...% rowN1 % dim2 % N2 % ... + bool load_binary( std::string filename ) + { + std::ifstream input_stream( filename.c_str( ), std::ios_base::binary | std::ios_base::in ); + if( input_stream.fail( ) ) + return false; + + int64_t nr_columns; + input_stream.read( (char*)&nr_columns, sizeof( int64_t ) ); + this->set_num_cols( (index)nr_columns ); + + column temp_col; + for( index cur_col = 0; cur_col < nr_columns; cur_col++ ) { + int64_t cur_dim; + input_stream.read( (char*)&cur_dim, sizeof( int64_t ) ); + this->set_dim( cur_col, (dimension)cur_dim ); + int64_t nr_rows; + input_stream.read( (char*)&nr_rows, sizeof( int64_t ) ); + temp_col.resize( ( std::size_t )nr_rows ); + for( index idx = 0; idx < nr_rows; idx++ ) { + int64_t cur_row; + input_stream.read( (char*)&cur_row, sizeof( int64_t ) ); + temp_col[ idx ] = (index)cur_row; + } + this->set_col( cur_col, temp_col ); + } + + input_stream.close( ); + return true; + } + + // Saves the boundary_matrix to given file in binary format + // Format: nr_columns % dim1 % N1 % row1 row2 % ...% rowN1 % dim2 % N2 % ... + bool save_binary( std::string filename ) + { + std::ofstream output_stream( filename.c_str( ), std::ios_base::binary | std::ios_base::out ); + if( output_stream.fail( ) ) + return false; + + const int64_t nr_columns = this->get_num_cols( ); + output_stream.write( (char*)&nr_columns, sizeof( int64_t ) ); + column tempCol; + for( index cur_col = 0; cur_col < nr_columns; cur_col++ ) { + int64_t cur_dim = this->get_dim( cur_col ); + output_stream.write( (char*)&cur_dim, sizeof( int64_t ) ); + this->get_col( cur_col, tempCol ); + int64_t cur_nr_rows = tempCol.size( ); + output_stream.write( (char*)&cur_nr_rows, sizeof( int64_t ) ); + for( index cur_row_idx = 0; cur_row_idx < (index)tempCol.size( ); cur_row_idx++ ) { + int64_t cur_row = tempCol[ cur_row_idx ]; + output_stream.write( (char*)&cur_row, sizeof( int64_t ) ); + } + } + + output_stream.close( ); + return true; + } + }; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/compute_persistence_pairs.h b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/compute_persistence_pairs.h new file mode 100644 index 0000000..48be65c --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/compute_persistence_pairs.h @@ -0,0 +1,128 @@ +/* Copyright 2013 IST Austria + Contributed by: Ulrich Bauer, Michael Kerber, Jan Reininghaus + + This file is part of PHAT. + + PHAT is free software: you can redistribute it and/or modify + it under the terms of the GNU Lesser General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + PHAT is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Lesser General Public License for more details. + + You should have received a copy of the GNU Lesser General Public License + along with PHAT. If not, see . */ + +#pragma once + +#include +#include +#include +#include + +namespace phat { + // Extracts persistence pairs in separate dimensions from a reduced + // boundary matrix representing ``double`` filtration. The pairs + // give persistent relative homology of the pair of filtrations. + // TODO: Use it with standard reduction algorithm (no template option). + template< typename ReductionAlgorithm, typename Representation > + void compute_relative_persistence_pairs(std::vector& pairs, boundary_matrix& boundary_matrix, const std::map& L) { + ReductionAlgorithm reduce; + reduce(boundary_matrix); + std::map free; + std::map invL; + for (std::map::const_iterator it = L.begin(); it != L.end(); ++it) { invL[it->second] = it->first; } + for (std::vector::iterator it = pairs.begin(); it != pairs.end(); ++it) { it->clear(); } + for (index idx = 0; idx < boundary_matrix.get_num_cols(); ++idx) { + int dimension = boundary_matrix.get_dim(idx); + if (L.find(idx) != L.end()) { ++dimension; } + free[idx] = true; + if (!boundary_matrix.is_empty(idx)) { + index birth = boundary_matrix.get_max_index(idx); + index death = idx; + pairs[dimension-1].append_pair(birth, death); + free[birth] = false; + free[death] = false; + } else { + // This is an L-simplex and a (dimension+1)-dimensional cycle + if (L.find(idx) != L.end()) { + assert(dimension < pairs.size()); + pairs[dimension].append_pair(idx, -1); + } + } + } + for (std::map::iterator it = free.begin(); it != free.end(); ++it) { + if (it->second) { + int dimension = boundary_matrix.get_dim(it->first); + if (invL.find(it->first) == invL.end() && L.find(it->first) == L.end()) { + assert(dimension < pairs.size()); + pairs[dimension].append_pair(it->first, -1); + } + } + } + } + + // Extracts persistence pairs in separate dimensions; expects a d-dimensional vector of persistent_pairs + template< typename ReductionAlgorithm, typename Representation > + void compute_persistence_pairs(std::vector& pairs, boundary_matrix& boundary_matrix) { + ReductionAlgorithm reduce; + reduce(boundary_matrix); + std::map free; + for (std::vector::iterator it = pairs.begin(); it != pairs.end(); ++it) { it->clear(); } + for (index idx = 0; idx < boundary_matrix.get_num_cols(); ++idx) { + int dimension = boundary_matrix.get_dim(idx); + free[idx] = true; + if (!boundary_matrix.is_empty(idx)) { + index birth = boundary_matrix.get_max_index(idx); + index death = idx; + pairs[dimension-1].append_pair(birth, death); + // Cannot be of the form (a, infinity) + free[birth] = false; + free[death] = false; + } + } + for (std::map::iterator it = free.begin(); it != free.end(); ++it) { + if (it->second) { + int dimension = boundary_matrix.get_dim(it->first); + pairs[dimension].append_pair(it->first, -1); + } + } + } + + template< typename ReductionAlgorithm, typename Representation > + void compute_persistence_pairs( persistence_pairs& pairs, boundary_matrix< Representation >& boundary_matrix ) { + ReductionAlgorithm reduce; + reduce( boundary_matrix ); + pairs.clear(); + for( index idx = 0; idx < boundary_matrix.get_num_cols(); idx++ ) { + if( !boundary_matrix.is_empty( idx ) ) { + index birth = boundary_matrix.get_max_index( idx ); + index death = idx; + pairs.append_pair( birth, death ); + } + } + } + + template< typename ReductionAlgorithm, typename Representation > + void compute_persistence_pairs_dualized( persistence_pairs& pairs, boundary_matrix< Representation >& boundary_matrix ) { + + dualize( boundary_matrix ); + compute_persistence_pairs< ReductionAlgorithm >( pairs, boundary_matrix ); + dualize_persistence_pairs( pairs, boundary_matrix.get_num_cols() ); + } + + template< typename Representation > + void compute_persistence_pairs( persistence_pairs& pairs, boundary_matrix< Representation >& boundary_matrix ) { + phat::compute_persistence_pairs< twist_reduction >( pairs, boundary_matrix ); + } + + + template< typename Representation > + void compute_persistence_pairs_dualized( persistence_pairs& pairs, boundary_matrix< Representation >& boundary_matrix ) { + compute_persistence_pairs_dualized< twist_reduction >( pairs, boundary_matrix ); + } + +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/helpers/dualize.h b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/helpers/dualize.h new file mode 100644 index 0000000..3ffedf8 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/helpers/dualize.h @@ -0,0 +1,74 @@ +/* Copyright 2013 IST Austria + Contributed by: Ulrich Bauer, Michael Kerber, Jan Reininghaus + + This file is part of PHAT. + + PHAT is free software: you can redistribute it and/or modify + it under the terms of the GNU Lesser General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + PHAT is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Lesser General Public License for more details. + + You should have received a copy of the GNU Lesser General Public License + along with PHAT. If not, see . */ + +#pragma once + +#include +#include +#include + + +namespace phat { + template< typename Representation > + void dualize( boundary_matrix< Representation >& boundary_matrix ) { + + std::vector< dimension > dual_dims; + std::vector< std::vector< index > > dual_matrix; + + index nr_of_columns = boundary_matrix.get_num_cols(); + dual_matrix.resize( nr_of_columns ); + dual_dims.resize( nr_of_columns ); + + std::vector< index > dual_sizes( nr_of_columns, 0 ); + + column temp_col; + for( index cur_col = 0; cur_col < nr_of_columns; cur_col++ ) { + boundary_matrix.get_col( cur_col, temp_col ); + for( index idx = 0; idx < (index)temp_col.size(); idx++) + dual_sizes[ nr_of_columns - 1 - temp_col[ idx ] ]++; + } + + #pragma omp parallel for + for( index cur_col = 0; cur_col < nr_of_columns; cur_col++ ) + dual_matrix[cur_col].reserve(dual_sizes[cur_col]); + + for( index cur_col = 0; cur_col < nr_of_columns; cur_col++ ) { + boundary_matrix.get_col( cur_col, temp_col ); + for( index idx = 0; idx < (index)temp_col.size(); idx++) + dual_matrix[ nr_of_columns - 1 - temp_col[ idx ] ].push_back( nr_of_columns - 1 - cur_col ); + } + + const dimension max_dim = boundary_matrix.get_max_dim(); + #pragma omp parallel for + for( index cur_col = 0; cur_col < nr_of_columns; cur_col++ ) + dual_dims[ nr_of_columns - 1 - cur_col ] = max_dim - boundary_matrix.get_dim( cur_col ); + + #pragma omp parallel for + for( index cur_col = 0; cur_col < nr_of_columns; cur_col++ ) + std::reverse( dual_matrix[ cur_col ].begin(), dual_matrix[ cur_col ].end() ); + + boundary_matrix.load_vector_vector( dual_matrix, dual_dims ); + } + + void dualize_persistence_pairs( persistence_pairs& pairs, const index n ) { + for (index i = 0; i < pairs.get_num_pairs(); ++i) { + std::pair< index, index > pair = pairs.get_pair( i ); + pairs.set_pair( i , n - 1 - pair.second, n - 1 - pair.first); + } + } +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/helpers/misc.h b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/helpers/misc.h new file mode 100644 index 0000000..fb5c07a --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/helpers/misc.h @@ -0,0 +1,75 @@ +/* Copyright 2013 IST Austria + Contributed by: Ulrich Bauer, Michael Kerber, Jan Reininghaus + + This file is part of PHAT. + + PHAT is free software: you can redistribute it and/or modify + it under the terms of the GNU Lesser General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + PHAT is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Lesser General Public License for more details. + + You should have received a copy of the GNU Lesser General Public License + along with PHAT. If not, see . */ + +#pragma once + +// STL includes +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +// VS2008 and below unfortunately do not support stdint.h +#if defined(_MSC_VER)&& _MSC_VER < 1600 + typedef __int8 int8_t; + typedef unsigned __int8 uint8_t; + typedef __int16 int16_t; + typedef unsigned __int16 uint16_t; + typedef __int32 int32_t; + typedef unsigned __int32 uint32_t; + typedef __int64 int64_t; + typedef unsigned __int64 uint64_t; +#else + #include +#endif + +// basic types. index can be changed to int32_t to save memory on small instances +namespace phat { + typedef int64_t index; + typedef int8_t dimension; + typedef std::vector< index > column; +} + +// OpenMP (proxy) functions +#if defined _OPENMP + #include +#else + #define omp_get_thread_num() 0 + #define omp_get_max_threads() 1 + #define omp_get_num_threads() 1 + void omp_set_num_threads( int ) {}; + #include + #define omp_get_wtime() (float)clock() / (float)CLOCKS_PER_SEC +#endif + +#include + + + diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/helpers/thread_local_storage.h b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/helpers/thread_local_storage.h new file mode 100644 index 0000000..d0b5332 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/helpers/thread_local_storage.h @@ -0,0 +1,52 @@ +/* Copyright 2013 IST Austria + Contributed by: Ulrich Bauer, Michael Kerber, Jan Reininghaus + + This file is part of PHAT. + + PHAT is free software: you can redistribute it and/or modify + it under the terms of the GNU Lesser General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + PHAT is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Lesser General Public License for more details. + + You should have received a copy of the GNU Lesser General Public License + along with PHAT. If not, see . */ + +#pragma once + +#include + +// should ideally be equal to the cache line size of the CPU +#define PHAT_TLS_SPACING_FACTOR 64 + +// ThreadLocalStorage with some spacing to avoid "false sharing" (see wikipedia) +template< typename T > +class thread_local_storage +{ +public: + + thread_local_storage() : per_thread_storage( omp_get_max_threads() * PHAT_TLS_SPACING_FACTOR ) {}; + + T& operator()() { + return per_thread_storage[ omp_get_thread_num() * PHAT_TLS_SPACING_FACTOR ]; + } + + const T& operator()() const { + return per_thread_storage[ omp_get_thread_num() * PHAT_TLS_SPACING_FACTOR ]; + } + + T& operator[]( int tid ) { + return per_thread_storage[ tid * PHAT_TLS_SPACING_FACTOR ]; + } + + const T& operator[]( int tid ) const { + return per_thread_storage[ tid * PHAT_TLS_SPACING_FACTOR ]; + } + +protected: + std::vector< T > per_thread_storage; +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/persistence_pairs.h b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/persistence_pairs.h new file mode 100644 index 0000000..eafc638 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/persistence_pairs.h @@ -0,0 +1,155 @@ +/* Copyright 2013 IST Austria + Contributed by: Ulrich Bauer, Michael Kerber, Jan Reininghaus + + This file is part of PHAT. + + PHAT is free software: you can redistribute it and/or modify + it under the terms of the GNU Lesser General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + PHAT is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Lesser General Public License for more details. + + You should have received a copy of the GNU Lesser General Public License + along with PHAT. If not, see . */ + +#pragma once + +#include + +namespace phat { + class persistence_pairs { + + protected: + std::vector< std::pair< index, index > > pairs; + + public: + index get_num_pairs() const { + return (index)pairs.size(); + } + + void append_pair( index birth, index death ) { + pairs.push_back( std::make_pair( birth, death ) ); + } + + std::pair< index, index > get_pair( index idx ) const { + return pairs[ idx ]; + } + + void set_pair( index idx, index birth, index death ) { + pairs[ idx ] = std::make_pair( birth, death ); + } + + void clear() { + pairs.clear(); + } + + void sort() { + std::sort( pairs.begin(), pairs.end() ); + } + + // Loads the persistence pairs from given file in asci format + // Format: nr_pairs % newline % birth1 % death1 % newline % birth2 % death2 % newline ... + bool load_ascii( std::string filename ) { + std::ifstream input_stream( filename.c_str() ); + if( input_stream.fail() ) + return false; + + int64_t nr_pairs; + input_stream >> nr_pairs; + pairs.clear(); + for( index idx = 0; idx < nr_pairs; idx++ ) { + int64_t birth; + input_stream >> birth; + int64_t death; + input_stream >> death; + append_pair( (index)birth, (index)death ); + } + + input_stream.close(); + return true; + } + + // Saves the persistence pairs to given file in binary format + // Format: nr_pairs % newline % birth1 % death1 % newline % birth2 % death2 % newline ... + bool save_ascii( std::string filename ) { + std::ofstream output_stream( filename.c_str() ); + if( output_stream.fail() ) + return false; + + this->sort(); + output_stream << get_num_pairs() << std::endl; + for( std::size_t idx = 0; idx < pairs.size(); idx++ ) { + output_stream << pairs[idx].first << " " << pairs[idx].second << std::endl; + } + + output_stream.close(); + return true; + } + + // Loads the persistence pairs from given file in binary format + // Format: nr_pairs % birth1 % death1 % birth2 % death2 ... + bool load_binary( std::string filename ) { + std::ifstream input_stream( filename.c_str(), std::ios_base::binary | std::ios_base::in ); + if( input_stream.fail() ) + return false; + + int64_t nr_pairs; + input_stream.read( (char*)&nr_pairs, sizeof( int64_t ) ); + for( index idx = 0; idx < nr_pairs; idx++ ) { + int64_t birth; + input_stream.read( (char*)&birth, sizeof( int64_t ) ); + int64_t death; + input_stream.read( (char*)&death, sizeof( int64_t ) ); + append_pair( (index)birth, (index)death ); + } + + input_stream.close(); + return true; + } + + // Saves the persistence pairs to given file in binary format + // Format: nr_pairs % birth1 % death1 % birth2 % death2 ... + bool save_binary( std::string filename ) { + std::ofstream output_stream( filename.c_str(), std::ios_base::binary | std::ios_base::out ); + if( output_stream.fail() ) + return false; + + this->sort(); + int64_t nr_pairs = get_num_pairs(); + output_stream.write( (char*)&nr_pairs, sizeof( int64_t ) ); + for( std::size_t idx = 0; idx < pairs.size(); idx++ ) { + int64_t birth = pairs[ idx ].first; + output_stream.write( (char*)&birth, sizeof( int64_t ) ); + int64_t death = pairs[ idx ].second; + output_stream.write( (char*)&death, sizeof( int64_t ) ); + } + + output_stream.close(); + return true; + } + + bool operator==( persistence_pairs& other_pairs ) { + this->sort(); + other_pairs.sort(); + if( pairs.size() != (std::size_t)other_pairs.get_num_pairs() ) + return false; + + for( index idx = 0; idx < (index)pairs.size(); idx++ ) + if( get_pair( idx ) != other_pairs.get_pair( idx ) ) + return false; + + return true; + } + + bool operator!=( persistence_pairs& other_pairs ) { + return !( *this == other_pairs ); + } + }; + + + +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/abstract_pivot_column.h b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/abstract_pivot_column.h new file mode 100644 index 0000000..e16d7a5 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/abstract_pivot_column.h @@ -0,0 +1,102 @@ +/* Copyright 2013 IST Austria + Contributed by: Ulrich Bauer, Michael Kerber, Jan Reininghaus + + This file is part of PHAT. + + PHAT is free software: you can redistribute it and/or modify + it under the terms of the GNU Lesser General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + PHAT is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Lesser General Public License for more details. + + You should have received a copy of the GNU Lesser General Public License + along with PHAT. If not, see . */ + +#pragma once + +#include +#include + +namespace phat { + + // Note: We could even make the rep generic in the underlying Const representation + // But I cannot imagine that anything else than vector> would + // make sense + template< typename PivotColumn > + class abstract_pivot_column : public vector_vector { + + protected: + typedef vector_vector Base; + typedef PivotColumn pivot_col; + + // For parallization purposes, it could be more than one full column + mutable thread_local_storage< pivot_col > pivot_cols; + mutable thread_local_storage< index > idx_of_pivot_cols; + + pivot_col& get_pivot_col() const { + return pivot_cols(); + } + + bool is_pivot_col( index idx ) const { + return idx_of_pivot_cols() == idx; + } + + void release_pivot_col() { + index idx = idx_of_pivot_cols(); + if( idx != -1 ) { + this->matrix[ idx ].clear(); + pivot_cols().get_col_and_clear( this->matrix[ idx ] ); + } + idx_of_pivot_cols() = -1; + } + + void make_pivot_col( index idx ) { + release_pivot_col(); + idx_of_pivot_cols() = idx; + get_pivot_col().add_col( matrix[ idx ] ); + } + + public: + + void _set_num_cols( index nr_of_cols ) { + #pragma omp parallel for + for( int tid = 0; tid < omp_get_num_threads(); tid++ ) { + pivot_cols[ tid ].init( nr_of_cols ); + idx_of_pivot_cols[ tid ] = -1; + } + Base::_set_num_cols( nr_of_cols ); + } + + void _add_to( index source, index target ) { + if( !is_pivot_col( target ) ) + make_pivot_col( target ); + get_pivot_col().add_col( matrix[source] ); + } + + void _sync() { + #pragma omp parallel for + for( int tid = 0; tid < omp_get_num_threads(); tid++ ) + release_pivot_col(); + } + + void _get_col( index idx, column& col ) const { is_pivot_col( idx ) ? get_pivot_col().get_col( col ) : Base::_get_col( idx, col ); } + + bool _is_empty( index idx ) const { return is_pivot_col( idx ) ? get_pivot_col().is_empty() : Base::_is_empty( idx ); } + + index _get_max_index( index idx ) const { return is_pivot_col( idx ) ? get_pivot_col().get_max_index() : Base::_get_max_index( idx ); } + + void _clear( index idx ) { is_pivot_col( idx ) ? get_pivot_col().clear() : Base::_clear( idx ); } + + void _set_col( index idx, const column& col ) { is_pivot_col( idx ) ? get_pivot_col().set_col( col ) : Base::_set_col( idx, col ); } + + void _remove_max( index idx ) { is_pivot_col( idx ) ? get_pivot_col().remove_max() : Base::_remove_max( idx ); } + + void finalize( index idx ) { Base::_finalize( idx ); } + }; +} + + diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/bit_tree_pivot_column.h b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/bit_tree_pivot_column.h new file mode 100644 index 0000000..4d48e88 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/bit_tree_pivot_column.h @@ -0,0 +1,165 @@ +/* Copyright 2013 IST Austria + Contributed by: Hubert Wagner + + This file is part of PHAT. + + PHAT is free software: you can redistribute it and/or modify + it under the terms of the GNU Lesser General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + PHAT is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Lesser General Public License for more details. + + You should have received a copy of the GNU Lesser General Public License + along with PHAT. If not, see . */ + +#pragma once + +#include +#include + +namespace phat { + + // This is a bitset indexed with a 64-ary tree. Each node in the index + // has 64 bits; i-th bit says that the i-th subtree is non-empty. + // Supports practically O(1), inplace, zero-allocation: insert, remove, max_element + // and clear in O(number of ones in the bitset). + // 'add_index' is still the real bottleneck in practice. + class bit_tree_column + { + protected: + + size_t offset; // data[i + offset] = ith block of the data-bitset + typedef uint64_t block_type; + std::vector< block_type > data; + + + size_t debrujin_magic_table[ 64 ]; + + enum { block_size_in_bits = 64 }; + enum { block_shift = 6 }; + + // Some magic: http://graphics.stanford.edu/~seander/bithacks.html + // Gets the position of the rightmost bit of 'x'. 0 means the most significant bit. + // (-x)&x isolates the rightmost bit. + // The whole method is much faster than calling log2i, and very comparable to using ScanBitForward/Reverse intrinsic, + // which should be one CPU instruction, but is not portable. + size_t rightmost_pos( const block_type value ) const { + return 64 - 1 - debrujin_magic_table[ ( (value & (-(int64_t)value) ) * 0x07EDD5E59A4E28C2 ) >> 58 ]; + } + + public: + + void init( index num_cols ) { + int64_t n = 1; // in case of overflow + int64_t bottom_blocks_needed = ( num_cols + block_size_in_bits - 1 ) / block_size_in_bits; + int64_t upper_blocks = 1; + + // How many blocks/nodes of index needed to index the whole bitset? + while( n * block_size_in_bits < bottom_blocks_needed ) { + n *= block_size_in_bits; + upper_blocks += n; + } + + offset = upper_blocks; + data.resize( upper_blocks + bottom_blocks_needed, 0 ); + + std::size_t temp_array[ 64 ] = { + 63, 0, 58, 1, 59, 47, 53, 2, + 60, 39, 48, 27, 54, 33, 42, 3, + 61, 51, 37, 40, 49, 18, 28, 20, + 55, 30, 34, 11, 43, 14, 22, 4, + 62, 57, 46, 52, 38, 26, 32, 41, + 50, 36, 17, 19, 29, 10, 13, 21, + 56, 45, 25, 31, 35, 16, 9, 12, + 44, 24, 15, 8, 23, 7, 6, 5 }; + + std::copy( &temp_array[ 0 ], &temp_array[ 64 ], &debrujin_magic_table[ 0 ] ); + } + + index get_max_index() const { + if( !data[ 0 ] ) + return -1; + + size_t n = 0; + size_t newn = 0; + size_t index = 0; + while( newn < data.size() ) { + n = newn; + index = rightmost_pos( data[ n ] ); + newn = ( n << block_shift ) + index + 1; + } + + return ( ( n - offset ) << block_shift ) + index; + } + + bool is_empty() const { + return data[ 0 ] == 0; + } + + void add_index( const size_t entry ) { + const block_type ONE = 1; + const block_type block_modulo_mask = ( ONE << block_shift ) - 1; + size_t index_in_level = entry >> block_shift; + size_t address = index_in_level + offset; + size_t index_in_block = entry & block_modulo_mask; + + block_type mask = ( ONE << ( block_size_in_bits - index_in_block - 1 ) ); + + data[ address ] ^= mask; + + // Check if we reached the root. Also, if anyone else was in this block, we don't need to update the path up. + while( address && !( data[ address ] & ~mask ) ) { + index_in_block = index_in_level & block_modulo_mask; + index_in_level >>= block_shift; + --address; + address >>= block_shift; + mask = ( ONE << ( block_size_in_bits - index_in_block - 1 ) ); + data[ address ] ^= mask; + } + } + + void get_col_and_clear( column &out ) { + index mx = this->get_max_index(); + while( mx != -1 ) { + out.push_back( mx ); + add_index( mx ); + mx = this->get_max_index(); + } + + std::reverse( out.begin(), out.end() ); + } + + void add_col(const column &col) { + for( size_t i = 0; i < col.size(); ++i ) + add_index(col[i]); + } + + void clear() { + index mx = this->get_max_index(); + while( mx != -1 ) { + add_index( mx ); + mx = this->get_max_index(); + } + } + + void remove_max() { + add_index( get_max_index() ); + } + + void set_col( const column& col ) { + clear(); + add_col( col ); + } + + void get_col( column& col ) { + get_col_and_clear( col ); + add_col( col ); + } + }; + + typedef abstract_pivot_column bit_tree_pivot_column; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/full_pivot_column.h b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/full_pivot_column.h new file mode 100644 index 0000000..c2e9e3c --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/full_pivot_column.h @@ -0,0 +1,100 @@ +/* Copyright 2013 IST Austria + Contributed by: Ulrich Bauer, Michael Kerber, Jan Reininghaus + + This file is part of PHAT. + + PHAT is free software: you can redistribute it and/or modify + it under the terms of the GNU Lesser General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + PHAT is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Lesser General Public License for more details. + + You should have received a copy of the GNU Lesser General Public License + along with PHAT. If not, see . */ + +#pragma once + +#include +#include + +namespace phat { + class full_column { + + protected: + std::priority_queue< index > history; + std::vector< char > is_in_history; + std::vector< char > col_bit_field; + + public: + void init( const index total_size ) { + col_bit_field.resize( total_size, false ); + is_in_history.resize( total_size, false ); + } + + void add_col( const column& col ) { + for( index idx = 0; idx < (index) col.size(); idx++ ) { + add_index( col[ idx ] ); + } + } + + void add_index( const index idx ) { + if( !is_in_history[ idx ] ) { + history.push( idx ); + is_in_history[ idx ] = true; + } + + col_bit_field[ idx ] = !col_bit_field[ idx ]; + } + + index get_max_index() { + while( history.size() > 0 ) { + index topIndex = history.top(); + if( col_bit_field[ topIndex ] ) { + return topIndex; + } else { + history.pop(); + is_in_history[ topIndex ] = false; + } + } + + return -1; + } + + void get_col_and_clear( column& col ) { + while( !is_empty() ) { + col.push_back( get_max_index() ); + add_index( get_max_index() ); + } + std::reverse( col.begin(), col.end() ); + } + + bool is_empty() { + return (get_max_index() == -1); + } + + void clear() { + while( !is_empty() ) + add_index( get_max_index() ); + } + + void remove_max() { + add_index( get_max_index() ); + } + + void set_col( const column& col ) { + clear(); + add_col( col ); + } + + void get_col( column& col ) { + get_col_and_clear( col ); + add_col( col ); + } + }; + + typedef abstract_pivot_column< full_column > full_pivot_column; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/heap_pivot_column.h b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/heap_pivot_column.h new file mode 100644 index 0000000..33cd07b --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/heap_pivot_column.h @@ -0,0 +1,126 @@ +/* Copyright 2013 IST Austria + Contributed by: Ulrich Bauer, Michael Kerber, Jan Reininghaus + + This file is part of PHAT. + + PHAT is free software: you can redistribute it and/or modify + it under the terms of the GNU Lesser General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + PHAT is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Lesser General Public License for more details. + + You should have received a copy of the GNU Lesser General Public License + along with PHAT. If not, see . */ + +#pragma once + +#include +#include + +namespace phat { + class heap_column { + + protected: + std::priority_queue< index > data; + + column temp_col; + index inserts_since_last_prune; + + void prune() + { + temp_col.clear( ); + index max_index = pop_max_index( ); + while( max_index != -1 ) { + temp_col.push_back( max_index ); + max_index = pop_max_index( ); + } + + for( index idx = 0; idx < (index)temp_col.size( ); idx++ ) + data.push( temp_col[ idx ] ); + + inserts_since_last_prune = 0; + } + + index pop_max_index() + { + if( data.empty( ) ) + return -1; + else { + index max_element = data.top( ); + data.pop(); + while( !data.empty( ) && data.top( ) == max_element ) { + data.pop( ); + if( data.empty( ) ) + return -1; + else { + max_element = data.top( ); + data.pop( ); + } + } + return max_element; + } + } + + public: + void init( const index total_size ) { + inserts_since_last_prune = 0; + clear(); + } + + void add_col( const column& col ) { + for( index idx = 0; idx < (index) col.size(); idx++ ) + data.push( col[ idx ] ); + inserts_since_last_prune += col.size( ); + if( 2 * inserts_since_last_prune >( index ) data.size( ) ) + prune(); + } + + index get_max_index() { + index max_element = pop_max_index( ); + if( max_element == -1 ) + return -1; + else { + data.push( max_element ); + return max_element; + } + } + + void get_col_and_clear( column& col ) { + col.clear(); + index max_index = pop_max_index( ); + while( max_index != -1 ) { + col.push_back( max_index ); + max_index = pop_max_index( ); + } + std::reverse( col.begin(), col.end() ); + } + + bool is_empty() { + return get_max_index() == -1; + } + + void clear() { + data = std::priority_queue< index >(); + } + + void remove_max() { + pop_max_index(); + } + + void set_col( const column& col ) { + clear(); + add_col( col ); + } + + void get_col( column& col ) { + get_col_and_clear( col ); + add_col( col ); + } + }; + + typedef abstract_pivot_column< heap_column > heap_pivot_column; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/sparse_pivot_column.h b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/sparse_pivot_column.h new file mode 100644 index 0000000..390fd91 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/sparse_pivot_column.h @@ -0,0 +1,79 @@ +/* Copyright 2013 IST Austria + Contributed by: Ulrich Bauer, Michael Kerber, Jan Reininghaus + + This file is part of PHAT. + + PHAT is free software: you can redistribute it and/or modify + it under the terms of the GNU Lesser General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + PHAT is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Lesser General Public License for more details. + + You should have received a copy of the GNU Lesser General Public License + along with PHAT. If not, see . */ + +#pragma once + +#include +#include + +namespace phat { + class sparse_column { + + protected: + std::set< index > data; + + void add_index( const index idx ) { + std::pair< std::set< index >::iterator, bool > result = data.insert( idx ); + if( result.second == false ) + data.erase( result.first ); + } + + public: + void init( const index total_size ) { + data.clear(); + } + + void add_col( const column& col ) { + for( index idx = 0; idx < (index) col.size(); idx++ ) + add_index( col[ idx ] ); + } + + index get_max_index() { + return data.empty() ? -1 : *data.rbegin(); + } + + void get_col_and_clear( column& col ) { + col.assign( data.begin(), data.end() ); + data.clear(); + } + + bool is_empty() { + return data.empty(); + } + + void clear() { + data.clear(); + } + + void remove_max() { + add_index( get_max_index() ); + } + + void set_col( const column& col ) { + clear(); + add_col( col ); + } + + void get_col( column& col ) { + get_col_and_clear( col ); + add_col( col ); + } + }; + + typedef abstract_pivot_column< sparse_column > sparse_pivot_column; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/vector_heap.h b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/vector_heap.h new file mode 100644 index 0000000..db0420f --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/vector_heap.h @@ -0,0 +1,170 @@ +/* Copyright 2013 IST Austria +Contributed by: Jan Reininghaus + +This file is part of PHAT. + +PHAT is free software: you can redistribute it and/or modify +it under the terms of the GNU Lesser General Public License as published by +the Free Software Foundation, either version 3 of the License, or +(at your option) any later version. + +PHAT is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +GNU Lesser General Public License for more details. + +You should have received a copy of the GNU Lesser General Public License +along with PHAT. If not, see . */ + +#pragma once + +#include + +namespace phat { + class vector_heap { + + protected: + std::vector< dimension > dims; + std::vector< column > matrix; + + std::vector< index > inserts_since_last_prune; + + mutable thread_local_storage< column > temp_column_buffer; + + protected: + void _prune( index idx ) + { + column& col = matrix[ idx ]; + column& temp_col = temp_column_buffer(); + temp_col.clear(); + index max_index = _pop_max_index( col ); + while( max_index != -1 ) { + temp_col.push_back( max_index ); + max_index = _pop_max_index( col ); + } + col = temp_col; + std::reverse( col.begin( ), col.end( ) ); + std::make_heap( col.begin( ), col.end( ) ); + inserts_since_last_prune[ idx ] = 0; + } + + index _pop_max_index( index idx ) + { + return _pop_max_index( matrix[ idx ] ); + } + + index _pop_max_index( column& col ) const + { + if( col.empty( ) ) + return -1; + else { + index max_element = col.front( ); + std::pop_heap( col.begin( ), col.end( ) ); + col.pop_back( ); + while( !col.empty( ) && col.front( ) == max_element ) { + std::pop_heap( col.begin( ), col.end( ) ); + col.pop_back( ); + if( col.empty( ) ) + return -1; + else { + max_element = col.front( ); + std::pop_heap( col.begin( ), col.end( ) ); + col.pop_back( ); + } + } + return max_element; + } + } + + public: + // overall number of cells in boundary_matrix + index _get_num_cols( ) const + { + return (index)matrix.size( ); + } + void _set_num_cols( index nr_of_columns ) + { + dims.resize( nr_of_columns ); + matrix.resize( nr_of_columns ); + inserts_since_last_prune.assign( nr_of_columns, 0 ); + } + + // dimension of given index + dimension _get_dim( index idx ) const + { + return dims[ idx ]; + } + void _set_dim( index idx, dimension dim ) + { + dims[ idx ] = dim; + } + + // replaces(!) content of 'col' with boundary of given index + void _get_col( index idx, column& col ) const + { + temp_column_buffer( ) = matrix[ idx ]; + + index max_index = _pop_max_index( temp_column_buffer() ); + while( max_index != -1 ) { + col.push_back( max_index ); + max_index = _pop_max_index( temp_column_buffer( ) ); + } + std::reverse( col.begin( ), col.end( ) ); + } + void _set_col( index idx, const column& col ) + { + matrix[ idx ] = col; + std::make_heap( matrix[ idx ].begin( ), matrix[ idx ].end( ) ); + } + + // true iff boundary of given idx is empty + bool _is_empty( index idx ) const + { + return _get_max_index( idx ) == -1; + } + + // largest row index of given column idx (new name for lowestOne()) + index _get_max_index( index idx ) const + { + column& col = const_cast< column& >( matrix[ idx ] ); + index max_element = _pop_max_index( col ); + col.push_back( max_element ); + std::push_heap( col.begin( ), col.end( ) ); + return max_element; + } + + // removes the maximal index of a column + void _remove_max( index idx ) + { + _pop_max_index( idx ); + } + + // clears given column + void _clear( index idx ) + { + matrix[ idx ].clear( ); + } + + // syncronizes all data structures (essential for openmp stuff) + void _sync( ) {} + + // adds column 'source' to column 'target' + void _add_to( index source, index target ) + { + for( index idx = 0; idx < (index)matrix[ source ].size( ); idx++ ) { + matrix[ target ].push_back( matrix[ source ][ idx ] ); + std::push_heap( matrix[ target ].begin(), matrix[ target ].end() ); + } + inserts_since_last_prune[ target ] += matrix[ source ].size(); + + if( 2 * inserts_since_last_prune[ target ] > ( index )matrix[ target ].size() ) + _prune( target ); + } + + // finalizes given column + void _finalize( index idx ) { + _prune( idx ); + } + + }; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/vector_list.h b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/vector_list.h new file mode 100644 index 0000000..ca0b5b8 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/vector_list.h @@ -0,0 +1,101 @@ +/* Copyright 2013 IST Austria + Contributed by: Jan Reininghaus + + This file is part of PHAT. + + PHAT is free software: you can redistribute it and/or modify + it under the terms of the GNU Lesser General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + PHAT is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Lesser General Public License for more details. + + You should have received a copy of the GNU Lesser General Public License + along with PHAT. If not, see . */ + +#pragma once + +#include + +namespace phat { + class vector_list { + + protected: + std::vector< dimension > dims; + std::vector< std::list< index > > matrix; + + public: + // overall number of cells in boundary_matrix + index _get_num_cols() const { + return (index)matrix.size(); + } + void _set_num_cols( index nr_of_columns ) { + dims.resize( nr_of_columns ); + matrix.resize( nr_of_columns ); + } + + // dimension of given index + dimension _get_dim( index idx ) const { + return dims[ idx ]; + } + void _set_dim( index idx, dimension dim ) { + dims[ idx ] = dim; + } + + // replaces(!) content of 'col' with boundary of given index + void _get_col( index idx, column& col ) const { + col.clear(); + col.reserve( matrix[idx].size() ); + std::copy (matrix[idx].begin(), matrix[idx].end(), std::back_inserter(col) ); + } + + void _set_col( index idx, const column& col ) { + matrix[ idx ].clear(); + matrix[ idx ].resize( col.size() ); + std::copy (col.begin(), col.end(), matrix[ idx ].begin() ); + } + + // true iff boundary of given idx is empty + bool _is_empty( index idx ) const { + return matrix[ idx ].empty(); + } + + // largest row index of given column idx (new name for lowestOne()) + index _get_max_index( index idx ) const { + return matrix[ idx ].empty() ? -1 : *matrix[ idx ].rbegin(); + } + + // removes the maximal index of a column + void _remove_max( index idx ) { + std::list< index >::iterator it = matrix[ idx ].end(); + it--; + matrix[ idx ].erase( it ); + } + + // clears given column + void _clear( index idx ) { + matrix[ idx ].clear(); + } + + // syncronizes all data structures (essential for openmp stuff) + void _sync() {} + + // adds column 'source' to column 'target' + void _add_to( index source, index target ) { + std::list< index >& source_col = matrix[ source ]; + std::list< index >& target_col = matrix[ target ]; + std::list< index > temp_col; + target_col.swap( temp_col ); + std::set_symmetric_difference( temp_col.begin(), temp_col.end(), + source_col.begin(), source_col.end(), + std::back_inserter( target_col ) ); + } + + // finalizes given column + void _finalize( index idx ) { + } + }; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/vector_set.h b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/vector_set.h new file mode 100644 index 0000000..6878a27 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/vector_set.h @@ -0,0 +1,99 @@ +/* Copyright 2013 IST Austria + Contributed by: Ulrich Bauer, Michael Kerber, Jan Reininghaus + + This file is part of PHAT. + + PHAT is free software: you can redistribute it and/or modify + it under the terms of the GNU Lesser General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + PHAT is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Lesser General Public License for more details. + + You should have received a copy of the GNU Lesser General Public License + along with PHAT. If not, see . */ + +#pragma once + +#include + +namespace phat { + class vector_set { + + protected: + std::vector< dimension > dims; + std::vector< std::set< index > > matrix; + + public: + // overall number of cells in boundary_matrix + index _get_num_cols() const { + return (index)matrix.size(); + } + void _set_num_cols( index nr_of_columns ) { + dims.resize( nr_of_columns ); + matrix.resize( nr_of_columns ); + } + + // dimension of given index + dimension _get_dim( index idx ) const { + return dims[ idx ]; + } + void _set_dim( index idx, dimension dim ) { + dims[ idx ] = dim; + } + + // replaces(!) content of 'col' with boundary of given index + void _get_col( index idx, column& col ) const { + col.clear(); + col.reserve( matrix[idx].size() ); + std::copy (matrix[idx].begin(), matrix[idx].end(), std::back_inserter(col) ); + } + void _set_col( index idx, const column& col ) { + matrix[ idx ].clear(); + matrix[ idx ].insert( col.begin(), col.end() ); + } + + // true iff boundary of given idx is empty + bool _is_empty( index idx ) const { + return matrix[ idx ].empty(); + } + + // largest row index of given column idx (new name for lowestOne()) + index _get_max_index( index idx ) const { + return matrix[ idx ].empty() ? -1 : *matrix[ idx ].rbegin(); + } + + // removes the maximal index of a column + void _remove_max( index idx ) { + std::set< index >::iterator it = matrix[ idx ].end(); + it--; + matrix[ idx ].erase( it ); + } + + // clears given column + void _clear( index idx ) { + matrix[ idx ].clear(); + } + + // syncronizes all data structures (essential for openmp stuff) + void _sync() {} + + // adds column 'source' to column 'target' + void _add_to( index source, index target ) { + for( std::set< index >::iterator it = matrix[ source ].begin(); it != matrix[ source ].end(); it++ ) { + std::set< index >& col = matrix[ target ]; + std::pair< std::set< index >::iterator, bool > result = col.insert( *it ); + if( !result.second ) + col.erase( result.first ); + } + } + + // finalizes given column + void _finalize( index idx ) { + } + + }; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/vector_vector.h b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/vector_vector.h new file mode 100644 index 0000000..f111d6b --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/include/phat/representations/vector_vector.h @@ -0,0 +1,107 @@ +/* Copyright 2013 IST Austria + Contributed by: Ulrich Bauer, Michael Kerber, Jan Reininghaus + + This file is part of PHAT. + + PHAT is free software: you can redistribute it and/or modify + it under the terms of the GNU Lesser General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + PHAT is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Lesser General Public License for more details. + + You should have received a copy of the GNU Lesser General Public License + along with PHAT. If not, see . */ + +#pragma once + +#include + +namespace phat { + class vector_vector { + + protected: + std::vector< dimension > dims; + std::vector< column > matrix; + + thread_local_storage< column > temp_column_buffer; + + public: + // overall number of cells in boundary_matrix + index _get_num_cols() const { + return (index)matrix.size(); + } + void _set_num_cols( index nr_of_columns ) { + dims.resize( nr_of_columns ); + matrix.resize( nr_of_columns ); + } + + // dimension of given index + dimension _get_dim( index idx ) const { + return dims[ idx ]; + } + void _set_dim( index idx, dimension dim ) { + dims[ idx ] = dim; + } + + // replaces(!) content of 'col' with boundary of given index + void _get_col( index idx, column& col ) const { + col = matrix[ idx ]; + } + void _set_col( index idx, const column& col ) { + matrix[ idx ] = col; + } + + // true iff boundary of given idx is empty + bool _is_empty( index idx ) const { + return matrix[ idx ].empty(); + } + + // largest row index of given column idx (new name for lowestOne()) + index _get_max_index( index idx ) const { + return matrix[ idx ].empty() ? -1 : matrix[ idx ].back(); + } + + // removes the maximal index of a column + void _remove_max( index idx ) { + matrix[ idx ].pop_back(); + } + + // clears given column + void _clear( index idx ) { + matrix[ idx ].clear(); + } + + // syncronizes all data structures (essential for openmp stuff) + void _sync() {} + + // adds column 'source' to column 'target' + void _add_to( index source, index target ) { + column& source_col = matrix[ source ]; + column& target_col = matrix[ target ]; + column& temp_col = temp_column_buffer(); + + + size_t new_size = source_col.size() + target_col.size(); + + if (new_size > temp_col.size()) temp_col.resize(new_size); + + std::vector::iterator col_end = std::set_symmetric_difference( target_col.begin(), target_col.end(), + source_col.begin(), source_col.end(), + temp_col.begin() ); + temp_col.erase(col_end, temp_col.end()); + + + target_col.swap(temp_col); + } + + // finalizes given column + void _finalize( index idx ) { + column& col = matrix[ idx ]; + column(col.begin(), col.end()).swap(col); + } + }; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/src/benchmark.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/src/benchmark.cpp new file mode 100644 index 0000000..24b5cfd --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/src/benchmark.cpp @@ -0,0 +1,343 @@ +/* Copyright 2013 IST Austria + Contributed by: Jan Reininghaus + + This file is part of PHAT. + + PHAT is free software: you can redistribute it and/or modify + it under the terms of the GNU Lesser General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + PHAT is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Lesser General Public License for more details. + + You should have received a copy of the GNU Lesser General Public License + along with PHAT. If not, see . */ + +#include + +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include + +#include + +#include +#include + + +enum Representation_type { VECTOR_VECTOR, VECTOR_HEAP, VECTOR_SET, SPARSE_PIVOT_COLUMN, HEAP_PIVOT_COLUMN, FULL_PIVOT_COLUMN, BIT_TREE_PIVOT_COLUMN, VECTOR_LIST }; +enum Algorithm_type {STANDARD, TWIST, ROW, CHUNK, CHUNK_SEQUENTIAL, SPECTRAL_SEQUENCE}; +enum Ansatz_type {PRIMAL, DUAL}; + +void print_help() { + std::cerr << "Usage: " << "benchmark " << "[options] input_filename_0 input_filename_1 ... input_filename_N" << std::endl; + std::cerr << std::endl; + std::cerr << "Options:" << std::endl; + std::cerr << std::endl; + std::cerr << "--latex -- produces Latex tables" << std::endl; + std::cerr << "--ascii -- use ascii file format" << std::endl; + std::cerr << "--binary -- use binary file format (default)" << std::endl; + std::cerr << "--help -- prints this screen" << std::endl; + std::cerr << "--dual -- use only dualization approach" << std::endl; + std::cerr << "--primal -- use only primal approach" << std::endl; + std::cerr << "--vector_vector, --vector_heap, --vector_set, --vector_list, --full_pivot_column, --sparse_pivot_column, --heap_pivot_column, --bit_tree_pivot_column -- use only a subset of representation data structures for boundary matrices" << std::endl; + std::cerr << "--standard, --twist, --chunk, --chunk_sequential, --spectral_sequence, --row -- use only a subset of reduction algorithms" << std::endl; +} + +void print_help_and_exit() { + print_help(); + exit( EXIT_FAILURE ); +} + +void parse_command_line( int argc, char** argv, bool& latex_tables_output, bool& use_binary, std::vector< Representation_type >& representations, std::vector< Algorithm_type >& algorithms + , std::vector< Ansatz_type >& ansaetze, std::vector< std::string >& input_filenames ) { + + if( argc < 2 ) print_help_and_exit(); + + int number_of_options = 0; + for( int idx = 1; idx < argc; idx++ ) { + const std::string argument = argv[ idx ]; + if( argument.size() > 2 && argument[ 0 ] == '-' && argument[ 1 ] == '-' ) { + if( argument == "--ascii" ) use_binary = false; + else if( argument == "--latex" ) latex_tables_output = true; + else if( argument == "--binary" ) use_binary = true; + else if( argument == "--vector_vector" ) representations.push_back( VECTOR_VECTOR ); + else if( argument == "--vector_heap" ) representations.push_back( VECTOR_HEAP ); + else if( argument == "--vector_set" ) representations.push_back( VECTOR_SET ); + else if( argument == "--vector_list" ) representations.push_back( VECTOR_LIST ); + else if( argument == "--full_pivot_column" ) representations.push_back( FULL_PIVOT_COLUMN ); + else if( argument == "--bit_tree_pivot_column" ) representations.push_back( BIT_TREE_PIVOT_COLUMN ); + else if( argument == "--sparse_pivot_column" ) representations.push_back( SPARSE_PIVOT_COLUMN ); + else if( argument == "--heap_pivot_column" ) representations.push_back( HEAP_PIVOT_COLUMN ); + else if( argument == "--standard" ) algorithms.push_back( STANDARD ); + else if( argument == "--twist" ) algorithms.push_back( TWIST ); + else if( argument == "--row" ) algorithms.push_back( ROW ); + else if( argument == "--chunk_sequential" ) algorithms.push_back( CHUNK_SEQUENTIAL ); + else if( argument == "--spectral_sequence" ) algorithms.push_back( SPECTRAL_SEQUENCE ); + else if( argument == "--chunk" ) algorithms.push_back( CHUNK ); + else if( argument == "--primal" ) ansaetze.push_back( PRIMAL ); + else if( argument == "--dual" ) ansaetze.push_back( DUAL ); + else if( argument == "--help" ) print_help_and_exit(); + else print_help_and_exit(); + } else { + input_filenames.push_back( argument ); + } + } + + if( representations.empty() == true ) { + representations.push_back( VECTOR_LIST ); + representations.push_back( VECTOR_VECTOR ); + representations.push_back( VECTOR_SET ); + representations.push_back( VECTOR_HEAP ); + representations.push_back( HEAP_PIVOT_COLUMN ); + representations.push_back( SPARSE_PIVOT_COLUMN ); + representations.push_back( FULL_PIVOT_COLUMN ); + representations.push_back( BIT_TREE_PIVOT_COLUMN ); + } + + if( algorithms.empty() == true ) { + algorithms.push_back( STANDARD ); + algorithms.push_back( TWIST ); + algorithms.push_back( ROW ); + algorithms.push_back( CHUNK ); + algorithms.push_back( SPECTRAL_SEQUENCE ); + // algorithms.push_back( CHUNK_SEQUENTIAL ); + } + + if( ansaetze.empty() == true ) { + ansaetze.push_back( PRIMAL ); + ansaetze.push_back( DUAL ); + } +} + +template +void benchmark( std::string input_filename, bool use_binary, Ansatz_type ansatz ) { + + phat::boundary_matrix< Representation > matrix; + bool read_successful = use_binary ? matrix.load_binary( input_filename ) : matrix.load_ascii( input_filename ); + + if( !read_successful ) { + std::cerr << std::endl << " Error opening file " << input_filename << std::endl; + print_help_and_exit(); + } + + Algorithm reduction_algorithm; + double reduction_timer = -1; + if( ansatz == PRIMAL ) { + std::cout << " primal,"; + reduction_timer = omp_get_wtime(); + reduction_algorithm( matrix ); + } else { + std::cout << " dual,"; + double dualization_timer = omp_get_wtime(); + dualize( matrix ); + double dualization_time = omp_get_wtime() - dualization_timer; + double dualization_time_rounded = floor( dualization_time * 10.0 + 0.5 ) / 10.0; + std::cout << " Dualization time: " << std::setiosflags( std::ios::fixed ) << std::setiosflags( std::ios::showpoint ) << std::setprecision( 1 ) << dualization_time_rounded <<"s,"; + reduction_timer = omp_get_wtime(); + reduction_algorithm( matrix ); + } + + double running_time = omp_get_wtime() - reduction_timer; + double running_time_rounded = floor( running_time * 10.0 + 0.5 ) / 10.0; + std::cout << " Reduction time: " << std::setiosflags( std::ios::fixed ) << std::setiosflags( std::ios::showpoint ) << std::setprecision( 1 ) << running_time_rounded <<"s" << std::endl; +} + +template +void benchmark_latex( std::string input_filename, bool use_binary, Ansatz_type ansatz ) +{ + phat::boundary_matrix< Representation > matrix; + bool read_successful = use_binary ? matrix.load_binary( input_filename ) : matrix.load_ascii( input_filename ); + + if( !read_successful ) { + std::cerr << std::endl << " Error opening file " << input_filename << std::endl; + print_help_and_exit( ); + } + + Algorithm reduction_algorithm; + double dualization_time = 0.0; + double reduction_timer = -1; + if( ansatz == PRIMAL ) { + reduction_timer = omp_get_wtime( ); + reduction_algorithm( matrix ); + } else { + double dualization_timer = omp_get_wtime( ); + dualize( matrix ); + dualization_time = omp_get_wtime( ) - dualization_timer; + reduction_timer = omp_get_wtime( ); + reduction_algorithm( matrix ); + } + + //double running_time = omp_get_wtime() - reduction_timer + dualization_time; + double running_time = omp_get_wtime( ) - reduction_timer; + double running_time_rounded = floor( running_time * 10.0 + 0.5 ) / 10.0; + std::cout << " && " << std::setiosflags( std::ios::fixed ) << std::setiosflags( std::ios::showpoint ) << std::setprecision( 1 ) << std::setw( 12 ) << running_time_rounded << std::setw( 1 ); +} + +#define COMPUTE(Representation) \ + std::cout << " " << #Representation << ","; \ + switch( algorithm ) { \ + case STANDARD: std::cout << " standard,"; benchmark< phat::Representation, phat::standard_reduction >( input_filename, use_binary, ansatz ); break; \ + case TWIST: std::cout << " twist,"; benchmark< phat::Representation, phat::twist_reduction >( input_filename, use_binary, ansatz ); break; \ + case ROW: std::cout << " row,"; benchmark< phat::Representation, phat::row_reduction >( input_filename, use_binary, ansatz ); break; \ + case CHUNK: std::cout << " chunk,"; benchmark< phat::Representation, phat::chunk_reduction >( input_filename, use_binary, ansatz ); break; \ + case SPECTRAL_SEQUENCE: std::cout << " spectral sequence,"; benchmark< phat::Representation, phat::spectral_sequence_reduction >( input_filename, use_binary, ansatz ); break; \ + case CHUNK_SEQUENTIAL: std::cout << " chunk_sequential,"; \ + int num_threads = omp_get_max_threads(); \ + omp_set_num_threads( 1 ); \ + benchmark< phat::Representation, phat::chunk_reduction >( input_filename, use_binary, ansatz ); \ + omp_set_num_threads( num_threads ); \ + break; \ + }; + +#define COMPUTE_LATEX(Representation) \ + switch( algorithm ) { \ + case STANDARD: benchmark_latex< phat::Representation, phat::standard_reduction >( input_filename, use_binary, ansatz ); break; \ + case TWIST: benchmark_latex< phat::Representation, phat::twist_reduction >( input_filename, use_binary, ansatz ); break; \ + case ROW: benchmark_latex< phat::Representation, phat::row_reduction >( input_filename, use_binary, ansatz ); break; \ + case CHUNK: benchmark_latex< phat::Representation, phat::chunk_reduction >( input_filename, use_binary, ansatz ); break; \ + case SPECTRAL_SEQUENCE: benchmark_latex< phat::Representation, phat::spectral_sequence_reduction >( input_filename, use_binary, ansatz ); break; \ + case CHUNK_SEQUENTIAL: int num_threads = omp_get_max_threads( ); \ + omp_set_num_threads( 1 ); \ + benchmark_latex< phat::Representation, phat::chunk_reduction >( input_filename, use_binary, ansatz ); \ + omp_set_num_threads( num_threads ); \ + break; \ + }; + +int main( int argc, char** argv ) +{ + bool latex_tables_output = false; // produces output in latex format + bool use_binary = true; // interpret inputs as binary or ascii files + std::vector< std::string > input_filenames; // name of file that contains the boundary matrix + + std::vector< Representation_type > representations; // representation class + std::vector< Algorithm_type > algorithms; // reduction algorithm + std::vector< Ansatz_type > ansaetze; // primal / dual + + parse_command_line( argc, argv, latex_tables_output, use_binary, representations, algorithms, ansaetze, input_filenames ); + + if( !latex_tables_output ) { + for( int idx_input = 0; idx_input < input_filenames.size(); idx_input++ ) { + std::string input_filename = input_filenames[ idx_input ]; + for( int idx_algorithm = 0; idx_algorithm < algorithms.size(); idx_algorithm++ ) { + Algorithm_type algorithm = algorithms[ idx_algorithm ]; + for( int idx_representation = 0; idx_representation < representations.size(); idx_representation++ ) { + Representation_type representation = representations[ idx_representation ]; + for( int idx_ansatz = 0; idx_ansatz < ansaetze.size(); idx_ansatz++ ) { + Ansatz_type ansatz = ansaetze[ idx_ansatz ]; + std::cout << input_filename << ","; + switch( representation ) { + case VECTOR_VECTOR: COMPUTE(vector_vector) break; + case VECTOR_HEAP: COMPUTE( vector_heap ) break; + case VECTOR_SET: COMPUTE(vector_set) break; + case VECTOR_LIST: COMPUTE(vector_list) break; + case FULL_PIVOT_COLUMN: COMPUTE(full_pivot_column) break; + case BIT_TREE_PIVOT_COLUMN: COMPUTE(bit_tree_pivot_column) break; + case SPARSE_PIVOT_COLUMN: COMPUTE(sparse_pivot_column) break; + case HEAP_PIVOT_COLUMN: COMPUTE(heap_pivot_column) break; + } + } + } + } + } + } else { + for( int idx_input = 0; idx_input < input_filenames.size( ); idx_input++ ) { + std::cout << "\\begin{table}[ h ]" << std::endl; + std::cout << "\\begin{center}" << std::endl; + std::cout << "\\begin{tabularx}{\\textwidth}{"; + std::cout << "r"; + for( int idx = 0; idx < representations.size( ); idx++ ) + std::cout << "Xr"; + std::cout << "}" << std::endl; + + std::cout << std::setw( 23 ) << " " << std::setw( 1 ); + + for( int idx_representation = 0; idx_representation < representations.size( ); idx_representation++ ) { + Representation_type representation = representations[ idx_representation ]; + std::cout << " && " << std::setw( 12 ); + switch( representation ) { + case VECTOR_VECTOR: std::cout << "Vector"; break; + case VECTOR_HEAP: std::cout << "Heap"; break; + case VECTOR_SET: std::cout << "Set"; break; + case VECTOR_LIST: std::cout << "List"; break; + case FULL_PIVOT_COLUMN: std::cout << "P-Full"; break; + case BIT_TREE_PIVOT_COLUMN: std::cout << "P-Bit-Tree"; break; + case SPARSE_PIVOT_COLUMN: std::cout << "P-Set"; break; + case HEAP_PIVOT_COLUMN: std::cout << "P-Heap"; break; + } + std::cout << std::setw( 1 ); + } + std::cout << " \\\\" << std::endl; + std::cout << "\\hline" << std::endl; + + std::string input_filename = input_filenames[ idx_input ]; + for( int idx_algorithm = 0; idx_algorithm < algorithms.size( ); idx_algorithm++ ) { + Algorithm_type algorithm = algorithms[ idx_algorithm ]; + for( int idx_ansatz = 0; idx_ansatz < ansaetze.size(); idx_ansatz++ ) { + Ansatz_type ansatz = ansaetze[ idx_ansatz ]; + std::cout << std::setw( 23 ); + if( ansatz == PRIMAL ) { + switch( algorithm ) { + case STANDARD: std::cout << "standard"; break; + case TWIST: std::cout << "twist"; break; + case ROW: std::cout << "row"; break; + case CHUNK: std::cout << "chunk"; break; + case SPECTRAL_SEQUENCE: std::cout << "spectral sequence"; break; + case CHUNK_SEQUENTIAL: std::cout << "chunk-sequential"; break; + } + } else { + switch( algorithm ) { + case STANDARD: std::cout << "standard$^*$"; break; + case TWIST: std::cout << "twist$^*$"; break; + case ROW: std::cout << "row$^*$"; break; + case CHUNK: std::cout << "chunk$^*$"; break; + case SPECTRAL_SEQUENCE: std::cout << "spectral sequence$^*$"; break; + case CHUNK_SEQUENTIAL: std::cout << "chunk-sequential$^*$"; break; + } + } + std::cout << std::setw( 1 ); + + + for( int idx_representation = 0; idx_representation < representations.size(); idx_representation++ ) { + Representation_type representation = representations[ idx_representation ]; + switch( representation ) { + case VECTOR_VECTOR: COMPUTE_LATEX( vector_vector ) break; + case VECTOR_HEAP: COMPUTE_LATEX( vector_heap ) break; + case VECTOR_SET: COMPUTE_LATEX( vector_set ) break; + case VECTOR_LIST: COMPUTE_LATEX( vector_list ) break; + case FULL_PIVOT_COLUMN: COMPUTE_LATEX( full_pivot_column ) break; + case BIT_TREE_PIVOT_COLUMN: COMPUTE_LATEX( bit_tree_pivot_column ) break; + case SPARSE_PIVOT_COLUMN: COMPUTE_LATEX( sparse_pivot_column ) break; + case HEAP_PIVOT_COLUMN: COMPUTE_LATEX( heap_pivot_column ) break; + } + } + std::cout << " \\\\" << std::endl; + } + } + + std::cout << "\\end{tabularx}" << std::endl; + std::cout << "\\end{center}" << std::endl; + std::string sanitized_input_filename( input_filename ); + std::replace( sanitized_input_filename.begin( ), sanitized_input_filename.end( ), '_', '-' ); + std::cout << "\\caption{ " << sanitized_input_filename << " }" << std::endl; + std::cout << "\\label{phat:" << sanitized_input_filename << "}" << std::endl; + std::cout << "\\end{table}" << std::endl << std::endl; + } + } +} + + diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/src/convert.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/src/convert.cpp new file mode 100644 index 0000000..5b4d7a0 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/src/convert.cpp @@ -0,0 +1,85 @@ +/* Copyright 2013 IST Austria + Contributed by: Ulrich Bauer, Michael Kerber, Jan Reininghaus + + This file is part of PHAT. + + PHAT is free software: you can redistribute it and/or modify + it under the terms of the GNU Lesser General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + PHAT is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Lesser General Public License for more details. + + You should have received a copy of the GNU Lesser General Public License + along with PHAT. If not, see . */ + + +#include +#include +#include + +void print_help() { + std::cerr << "Usage: " << "convert " << "[options] input_filename output_filename" << std::endl; + std::cerr << std::endl; + std::cerr << "Options:" << std::endl; + std::cerr << std::endl; + std::cerr << "--ascii -- use ascii file format for input_filename" << std::endl; + std::cerr << "--binary -- use binary file format for input_filename (default)" << std::endl; + std::cerr << "--save-ascii -- use ascii file format for output_filename" << std::endl; + std::cerr << "--save-binary -- use binary file format for output_filename (default)" << std::endl; + std::cerr << "--dualize -- dualize filtration" << std::endl; + std::cerr << "--help -- prints this screen" << std::endl; +} + +void print_help_and_exit() { + print_help(); + exit( EXIT_FAILURE ); +} + +void parse_command_line( int argc, char** argv, bool& use_binary, bool& use_save_binary, bool& use_dualize, std::string& input_filename, std::string& output_filename) { + + if( argc < 3 ) print_help_and_exit(); + + input_filename = argv[ argc - 2 ]; + output_filename = argv[ argc - 1 ]; + + for( int idx = 1; idx < argc - 2; idx++ ) { + const std::string option = argv[ idx ]; + + if( option == "--dualize" ) use_dualize = true; + else if( option == "--ascii" ) use_binary = false; + else if( option == "--binary" ) use_binary = true; + else if( option == "--save-ascii" ) use_save_binary = false; + else if( option == "--save-binary" ) use_save_binary = true; + else if( option == "--help" ) print_help_and_exit(); + else print_help_and_exit(); + } +} + +int main( int argc, char** argv ) +{ + bool use_binary = true; // interpret input as binary or ascii file + bool use_save_binary = true; // write output as binary or ascii file + bool use_dualize = false; // dualize filtration + std::string input_filename; // name of file that contains the boundary matrix + std::string output_filename; // name of file that will contain the boundary matrix in the new binary format + + parse_command_line( argc, argv, use_binary, use_save_binary, use_dualize, input_filename, output_filename ); + + phat::boundary_matrix< phat::bit_tree_pivot_column > matrix; + if( use_binary ) + matrix.load_binary( input_filename ); + else + matrix.load_ascii( input_filename ); + + if( use_dualize ) + dualize( matrix ); + + if( use_save_binary ) + matrix.save_binary( output_filename ); + else + matrix.save_ascii( output_filename ); +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/src/info.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/src/info.cpp new file mode 100644 index 0000000..fd2cdad --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/src/info.cpp @@ -0,0 +1,77 @@ +/* Copyright 2013 IST Austria + Contributed by: Jan Reininghaus + + This file is part of PHAT. + + PHAT is free software: you can redistribute it and/or modify + it under the terms of the GNU Lesser General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + PHAT is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Lesser General Public License for more details. + + You should have received a copy of the GNU Lesser General Public License + along with PHAT. If not, see . */ + +#include + +void print_help() { + std::cerr << "Usage: " << "info " << "[options] input_filename_0 input_filename_1 ... input_filename_N" << std::endl; + std::cerr << std::endl; + std::cerr << "Options:" << std::endl; + std::cerr << std::endl; + std::cerr << "--ascii -- use ascii file format" << std::endl; + std::cerr << "--binary -- use binary file format (default)" << std::endl; + std::cerr << "--help -- prints this screen" << std::endl; +} + +void print_help_and_exit() { + print_help(); + exit( EXIT_FAILURE ); +} + +void parse_command_line( int argc, char** argv, bool& use_binary, std::vector< std::string >& input_filenames ) { + + if( argc < 2 ) print_help_and_exit(); + + int number_of_options = 0; + for( int idx = 1; idx < argc; idx++ ) { + const std::string argument = argv[ idx ]; + if( argument.size() > 2 && argument[ 0 ] == '-' && argument[ 1 ] == '-' ) { + if( argument == "--ascii" ) use_binary = false; + else if( argument == "--binary" ) use_binary = true; + else if( argument == "--help" ) print_help_and_exit(); + else print_help_and_exit(); + } else { + input_filenames.push_back( argument ); + } + } +} + +int main( int argc, char** argv ) +{ + bool use_binary = true; // interpret inputs as binary or ascii files + std::vector< std::string > input_filenames; // name of file that contains the boundary matrix + + parse_command_line( argc, argv, use_binary, input_filenames ); + + for( int idx_input = 0; idx_input < input_filenames.size(); idx_input++ ) { + std::string input_filename = input_filenames[ idx_input ]; + phat::boundary_matrix<> matrix; + bool read_successful = use_binary ? matrix.load_binary( input_filename ) : matrix.load_ascii( input_filename ); + if( !read_successful ) { + std::cerr << std::endl << " Error opening file " << input_filename << std::endl; + print_help_and_exit(); + } + + std::cout << input_filename << ":" << std::endl; + std::cout << "\t" << "num_cols = " << matrix.get_num_cols() << std::endl; + std::cout << "\t" << "max_dim = " << (int)matrix.get_max_dim() << std::endl; + std::cout << "\t" << "max_col_entries = " << matrix.get_max_col_entries() << std::endl; + std::cout << "\t" << "max_row_entries = " << matrix.get_max_row_entries() << std::endl; + std::cout << "\t" << "total_nr_of_entries = " << matrix.get_num_entries() << std::endl; + } +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/src/phat.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/src/phat.cpp new file mode 100644 index 0000000..3770cf3 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/src/phat.cpp @@ -0,0 +1,190 @@ +/* Copyright 2013 IST Austria + Contributed by: Ulrich Bauer, Michael Kerber, Jan Reininghaus + + This file is part of PHAT. + + PHAT is free software: you can redistribute it and/or modify + it under the terms of the GNU Lesser General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + PHAT is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Lesser General Public License for more details. + + You should have received a copy of the GNU Lesser General Public License + along with PHAT. If not, see . */ + +#include + +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include + +#include + +enum Representation_type { VECTOR_VECTOR, VECTOR_HEAP, VECTOR_SET, SPARSE_PIVOT_COLUMN, FULL_PIVOT_COLUMN, BIT_TREE_PIVOT_COLUMN, VECTOR_LIST, HEAP_PIVOT_COLUMN }; +enum Algorithm_type {STANDARD, TWIST, ROW, CHUNK, CHUNK_SEQUENTIAL, SPECTRAL_SEQUENCE }; + +void print_help() { + std::cerr << "Usage: " << "phat " << "[options] input_filename output_filename" << std::endl; + std::cerr << std::endl; + std::cerr << "Options:" << std::endl; + std::cerr << std::endl; + std::cerr << "--ascii -- use ascii file format" << std::endl; + std::cerr << "--binary -- use binary file format (default)" << std::endl; + std::cerr << "--help -- prints this screen" << std::endl; + std::cerr << "--verbose -- verbose output" << std::endl; + std::cerr << "--dualize -- use dualization approach" << std::endl; + std::cerr << "--vector_vector, --vector_heap, --vector_set, --vector_list, --full_pivot_column, --sparse_pivot_column, --heap_pivot_column, --bit_tree_pivot_column -- selects a representation data structure for boundary matrices (default is '--bit_tree_pivot_column')" << std::endl; + std::cerr << "--standard, --twist, --chunk, --chunk_sequential, --spectral_sequence, --row -- selects a reduction algorithm (default is '--twist')" << std::endl; +} + +void print_help_and_exit() { + print_help(); + exit( EXIT_FAILURE ); +} + +void parse_command_line( int argc, char** argv, bool& use_binary, Representation_type& representation, Algorithm_type& algorithm, + std::string& input_filename, std::string& output_filename, bool& verbose, bool& dualize) { + + if( argc < 3 ) print_help_and_exit(); + + input_filename = argv[ argc - 2 ]; + output_filename = argv[ argc - 1 ]; + + for( int idx = 1; idx < argc - 2; idx++ ) { + const std::string option = argv[ idx ]; + + if( option == "--ascii" ) use_binary = false; + else if( option == "--binary" ) use_binary = true; + else if( option == "--dualize" ) dualize = true; + else if( option == "--vector_vector" ) representation = VECTOR_VECTOR; + else if( option == "--vector_heap" ) representation = VECTOR_HEAP; + else if( option == "--vector_set" ) representation = VECTOR_SET; + else if( option == "--vector_list" ) representation = VECTOR_LIST; + else if( option == "--full_pivot_column" ) representation = FULL_PIVOT_COLUMN; + else if( option == "--bit_tree_pivot_column" ) representation = BIT_TREE_PIVOT_COLUMN; + else if( option == "--sparse_pivot_column" ) representation = SPARSE_PIVOT_COLUMN; + else if( option == "--heap_pivot_column" ) representation = HEAP_PIVOT_COLUMN; + else if( option == "--standard" ) algorithm = STANDARD; + else if( option == "--twist" ) algorithm = TWIST; + else if( option == "--row" ) algorithm = ROW; + else if( option == "--chunk" ) algorithm = CHUNK; + else if( option == "--chunk_sequential" ) algorithm = CHUNK_SEQUENTIAL; + else if( option == "--spectral_sequence" ) algorithm = SPECTRAL_SEQUENCE; + else if( option == "--verbose" ) verbose = true; + else if( option == "--help" ) print_help_and_exit(); + else print_help_and_exit(); + } +} + +#define LOG(msg) if( verbose ) std::cout << msg << std::endl; + +template +void compute_pairing( std::string input_filename, std::string output_filename, bool use_binary, bool verbose, bool dualize ) { + + phat::boundary_matrix< Representation > matrix; + bool read_successful; + + double read_timer = omp_get_wtime(); + if( use_binary ) { + LOG( "Reading input file " << input_filename << " in binary mode" ) + read_successful = matrix.load_binary( input_filename ); + } else { + LOG( "Reading input file " << input_filename << " in ascii mode" ) + read_successful = matrix.load_ascii( input_filename ); + } + double read_time = omp_get_wtime() - read_timer; + double read_time_rounded = floor( read_time * 10.0 + 0.5 ) / 10.0; + LOG( "Reading input file took " << std::setiosflags( std::ios::fixed ) << std::setiosflags( std::ios::showpoint ) << std::setprecision( 1 ) << read_time_rounded <<"s" ) + + if( !read_successful ) { + std::cerr << "Error opening file " << input_filename << std::endl; + print_help_and_exit(); + } + + phat::index num_cols = matrix.get_num_cols(); + + if( dualize ) { + double dualize_timer = omp_get_wtime(); + LOG( "Dualizing ..." ) + phat::dualize ( matrix ); + double dualize_time = omp_get_wtime() - dualize_timer; + double dualize_time_rounded = floor( dualize_time * 10.0 + 0.5 ) / 10.0; + LOG( "Dualizing took " << std::setiosflags( std::ios::fixed ) << std::setiosflags( std::ios::showpoint ) << std::setprecision( 1 ) << dualize_time_rounded <<"s" ) + } + + double pairs_timer = omp_get_wtime(); + phat::persistence_pairs pairs; + LOG( "Computing persistence pairs ..." ) + phat::compute_persistence_pairs < Algorithm > ( pairs, matrix ); + double pairs_time = omp_get_wtime() - pairs_timer; + double pairs_time_rounded = floor( pairs_time * 10.0 + 0.5 ) / 10.0; + LOG( "Computing persistence pairs took " << std::setiosflags( std::ios::fixed ) << std::setiosflags( std::ios::showpoint ) << std::setprecision( 1 ) << pairs_time_rounded <<"s" ) + + if( dualize ) dualize_persistence_pairs( pairs, num_cols ); + + + double write_timer = omp_get_wtime(); + if( use_binary ) { + LOG( "Writing output file " << output_filename << " in binary mode ..." ) + pairs.save_binary( output_filename ); + } else { + LOG( "Writing output file " << output_filename << " in ascii mode ..." ) + pairs.save_ascii( output_filename ); + } + double write_time = omp_get_wtime() - write_timer; + double write_time_rounded = floor( write_time * 10.0 + 0.5 ) / 10.0; + LOG( "Writing output file took " << std::setiosflags( std::ios::fixed ) << std::setiosflags( std::ios::showpoint ) << std::setprecision( 1 ) << write_time_rounded <<"s" ) +} + +#define COMPUTE_PAIRING(Representation) \ + switch( algorithm ) { \ + case STANDARD: compute_pairing< phat::Representation, phat::standard_reduction> ( input_filename, output_filename, use_binary, verbose, dualize ); break; \ + case TWIST: compute_pairing< phat::Representation, phat::twist_reduction> ( input_filename, output_filename, use_binary, verbose, dualize ); break; \ + case ROW: compute_pairing< phat::Representation, phat::row_reduction >( input_filename, output_filename, use_binary, verbose, dualize ); break; \ + case SPECTRAL_SEQUENCE: compute_pairing< phat::Representation, phat::spectral_sequence_reduction >( input_filename, output_filename, use_binary, verbose, dualize ); break; \ + case CHUNK: compute_pairing< phat::Representation, phat::chunk_reduction >( input_filename, output_filename, use_binary, verbose, dualize ); break; \ + case CHUNK_SEQUENTIAL: int num_threads = omp_get_max_threads(); \ + omp_set_num_threads( 1 ); \ + compute_pairing< phat::Representation, phat::chunk_reduction >( input_filename, output_filename, use_binary, verbose, dualize ); break; \ + omp_set_num_threads( num_threads ); \ + break; \ + } + +int main( int argc, char** argv ) +{ + bool use_binary = true; // interpret input as binary or ascii file + Representation_type representation = BIT_TREE_PIVOT_COLUMN; // representation class + Algorithm_type algorithm = TWIST; // reduction algorithm + std::string input_filename; // name of file that contains the boundary matrix + std::string output_filename; // name of file that will contain the persistence pairs + bool verbose = false; // print timings / info + bool dualize = false; // toggle for dualization approach + + parse_command_line( argc, argv, use_binary, representation, algorithm, input_filename, output_filename, verbose, dualize ); + + switch( representation ) { + case VECTOR_VECTOR: COMPUTE_PAIRING(vector_vector) break; + case VECTOR_HEAP: COMPUTE_PAIRING( vector_heap ) break; + case VECTOR_SET: COMPUTE_PAIRING(vector_set) break; + case VECTOR_LIST: COMPUTE_PAIRING(vector_list) break; + case FULL_PIVOT_COLUMN: COMPUTE_PAIRING(full_pivot_column) break; + case BIT_TREE_PIVOT_COLUMN: COMPUTE_PAIRING(bit_tree_pivot_column) break; + case SPARSE_PIVOT_COLUMN: COMPUTE_PAIRING(sparse_pivot_column) break; + case HEAP_PIVOT_COLUMN: COMPUTE_PAIRING(heap_pivot_column) break; + } +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/src/relative_example.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/src/relative_example.cpp new file mode 100644 index 0000000..9a6798a --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/src/relative_example.cpp @@ -0,0 +1,462 @@ +#include + +// main data structure (choice affects performance) +#include + +// algorithm (choice affects performance) +#include +#include +#include +#include + +#include +#include +#include + +class Simplex { +public: + Simplex(const int& i, const double& t, const int& d, const bool& L) + : idx_(i), time_(t), dim_(d), inL_(L) + { } + + void add_bd(const int& idx) { boundary_.push_back(idx); } + + int idx() const { return idx_; } + double time() const { return time_; } + int dim() const { return dim_; } + bool inL() const { return inL_; } + std::vector bd() const { return boundary_; } + + bool operator<(const Simplex& rhs) const { + bool returnP = (time() < rhs.time()) || + (time() == rhs.time() && dim() < rhs.dim()) || + (time() == rhs.time() && dim() == rhs.dim() && !inL() && rhs.inL()); + return returnP; + } + bool operator<=(const Simplex& rhs) const { + return *this == rhs || *this < rhs; + } + bool operator==(const Simplex& rhs) const { + // It should suffice to only check whether idx()==rhs.idx() + return idx() == rhs.idx() && time() == rhs.time() && dim() == rhs.dim() && inL() == rhs.inL() && bd() == rhs.bd(); + } + bool operator!=(const Simplex& rhs) const { + return !(*this == rhs); + } + std::string str() const { + std::string s = std::to_string(idx_) + " " + std::to_string(time_) + " " + std::to_string(dim_) + " " + std::to_string(inL_) + " "; + for (int i = 0; i < boundary_.size(); ++i) { s += std::to_string(boundary_[i]) + " "; } + return s; + } +private: + int idx_; // index representing the simplex + double time_; // the time the simplex enters the filtration + int dim_; // the dimension of the simplex + bool inL_; // whether the simplex is in L; if false, it is in K + std::vector boundary_; // boundary of the simplex +}; + +// Given a filtration in the vector simplices, construct the boundary matrix and compute relative persistence +void compute_relative_persistence(std::vector& simplices, const std::map& L, std::vector& pairs); +void example(); +void load(std::string prefix, std::vector& v, std::map& L, int& mx_dim); +void experiments(std::string prefix); +void build_bdm(std::vector& v, std::map& L, phat::boundary_matrix< phat::vector_vector >& bd_m); +void test(); + +int main(int argc, char** argv) { + // experiments("hi_3_0p9"); + if (argc <= 1) { return 1; } + experiments(argv[1]); + // experiments("hi_0p94_0p9_0p5_3"); + // test(); + // experiments("test-2"); + return 1; + example(); + return 1; + // first define a boundary matrix with the chosen internal representation + phat::boundary_matrix< phat::vector_vector > bd_m; + + // set the number of columns, which equals the number of simplices in the + // simplicial complex + bd_m.set_num_cols(8); + + // for each column, i, set the dimension of the simplex it represents + bd_m.set_dim(0, 0); // a_K + bd_m.set_dim(1, 0); // b_K + bd_m.set_dim(2, 0); // c_K + bd_m.set_dim(3, 1); // ab_K + bd_m.set_dim(4, 1); // ac_K + bd_m.set_dim(5, 1); // bc_K + bd_m.set_dim(6, 0); // a_L + bd_m.set_dim(7, 0); // b_L + + std::vector< phat::index > tmp_col; + + // Define boundary of the 0-dimensional simplices, labeled with integers 0 through 2 + bd_m.set_col(0, tmp_col); + bd_m.set_col(1, tmp_col); + bd_m.set_col(2, tmp_col); + + // Now add the 2-dimensional simplex 3 whose boundary consists of 0-dimensional simplices 0 and 1 + tmp_col.push_back(0); + tmp_col.push_back(1); + bd_m.set_col(3, tmp_col); + + // Empty the vector + tmp_col.clear(); + + // Repeat for 1-dimensional simpliex 4 + tmp_col.push_back(0); + tmp_col.push_back(2); + bd_m.set_col(4, tmp_col); + + tmp_col.clear(); + + tmp_col.push_back(1); + tmp_col.push_back(2); + bd_m.set_col(5, tmp_col); + + tmp_col.clear(); + + tmp_col.push_back(0); + bd_m.set_col(6, tmp_col); + + tmp_col.clear(); + + tmp_col.push_back(1); + bd_m.set_col(7, tmp_col); + + // Now compute the homology via matrix reduction + std::cout << "The boundary matrix has " << bd_m.get_num_cols() << " columns." << std::endl; + for (phat::index col_idx = 0; col_idx < bd_m.get_num_cols(); ++col_idx) { + std::cout << "Processing column corresponding to " << static_cast(bd_m.get_dim(col_idx)) << "-dimensional simplex." << std::endl; + if (!bd_m.is_empty(col_idx)) { // If column at the given index is not empty + std::vector tmp_col; + bd_m.get_col(col_idx, tmp_col); + std::cout << "Its boundary:" << std::endl; + for (phat::index idx = 0; idx < static_cast(tmp_col.size()); ++idx) { + std::cout << tmp_col[idx] << " "; + } + std::cout << std::endl; + } else { + std::cout << "Its boudary is the empty chain." << std::endl; + } + } + std::cout << "Overall, the matrix has " << bd_m.get_num_entries() << " entries." << std::endl; + + // Compute persistence + std::cout << "Persistence" << std::endl; + std::map L; L[6] = 0; L[7] = 1; + std::vector pp_v(2); + phat::compute_relative_persistence_pairs(pp_v, bd_m, L); + for (int d = 0; d < pp_v.size(); ++d) { + std::cout << "Dimension " << d << std::endl; + for (int idx = 0; idx < pp_v[d].get_num_pairs(); ++idx) { + phat::index birth = pp_v[d].get_pair(idx).first; + phat::index death = pp_v[d].get_pair(idx).second; + std::cout << "(" << birth << ", " << death << ")" << std::endl; + } + } + + return 0; +} + +// simplices is a vector of simplices, which, when ordered, represents the filtration +// L maps the label of each L-simplex to the label of the corresponding K-simplex +// pairs is a vector of persistence_pairs, whose d-th component corresponds to d-dimensional persistence pairs +void compute_relative_persistence(std::vector& simplices, const std::map& L, std::vector& pairs) { + std::map label_to_idx; // Maps each simplex to the column that represents that simplex + std::sort(simplices.begin(), simplices.end()); + const int N = simplices.size(); + int mx_dim = 0; // Dimension of the final complex + phat::boundary_matrix< phat::vector_vector > bd_m; + bd_m.set_num_cols(N); + // Set dimension of each of the simplices + for (int idx = 0; idx < simplices.size(); ++idx) { + bd_m.set_dim(idx, simplices[idx].dim()); + label_to_idx[simplices[idx].idx()] = idx; + mx_dim = std::max(mx_dim, simplices[idx].dim()); + } + // Set boundary for each simplex + std::vector< phat::index > tmp_col; + for (int idx = 0; idx < simplices.size(); ++idx) { + tmp_col.clear(); + if (simplices[idx].dim() > 0) { + // The boudary consists of column indices---use label_to_idx for that + std::vector tmp_bd = simplices[idx].bd(); + for (int jdx = 0; jdx < tmp_bd.size(); ++jdx) { + tmp_col.push_back(label_to_idx[tmp_bd[jdx]]); + } + } + // If an L-simplex, add the corresponding K-simplex to its boundary + if (simplices[idx].inL()) { + // Need to find the corresponding K-simplex + const int idx_K = L.find(simplices[idx].idx())->second; + tmp_col.push_back(label_to_idx[idx_K]); + } + sort(tmp_col.begin(), tmp_col.end()); + bd_m.set_col(idx, tmp_col); + } + // Compute persistence + //std::vector pp_v(mx_dim+1); + std::map L2; + for (std::map::const_iterator it = L.begin(); it != L.end(); ++it) { L2[label_to_idx[it->first]] = label_to_idx[it->second]; } + + // Now compute the homology via matrix reduction +/* + std::cout << "The boundary matrix has " << bd_m.get_num_cols() << " columns." << std::endl; + for (phat::index col_idx = 0; col_idx < bd_m.get_num_cols(); ++col_idx) { + std::cout << "Processing column corresponding to " << static_cast(bd_m.get_dim(col_idx)) << "-dimensional simplex." << std::endl; + if (!bd_m.is_empty(col_idx)) { // If column at the given index is not empty + std::vector tmp_col; + bd_m.get_col(col_idx, tmp_col); + std::cout << "Its boundary:" << std::endl; + for (phat::index idx = 0; idx < static_cast(tmp_col.size()); ++idx) { + std::cout << tmp_col[idx] << " "; + } + std::cout << std::endl; + } else { + std::cout << "Its boudary is the empty chain." << std::endl; + } + } + std::cout << "Overall, the matrix has " << bd_m.get_num_entries() << " entries." << std::endl; +*/ + phat::compute_relative_persistence_pairs(pairs, bd_m, L); +} + +void example() { + std::vector v; + v.push_back(Simplex(0, 0.1, 0, true)); // a_K + v.push_back(Simplex(1, 0.1, 0, false)); // a _L + v.push_back(Simplex(2, 0.4, 0, false)); // c_K + v.push_back(Simplex(3, 0.3, 0, true)); // b_L + v.push_back(Simplex(5, 0.3, 0, false)); // b_K + Simplex s(4, 0.3, 1, false); s.add_bd(1); s.add_bd(5); // ab_K + v.push_back(s); + std::sort(v.begin(), v.end()); + for (std::vector::iterator it = v.begin(); it != v.end(); ++it) { std::cout << "(" << it->idx() << ", " << it->time() << ", " << it->dim() << ") " << it->inL() << std::endl; } + + std::map ml; ml[0]=1; ml[3]=5; + std::vector pp_v(2); + + compute_relative_persistence(v, ml, pp_v); + + for (int d = 0; d < pp_v.size(); ++d) { + std::cout << "Dimension " << d << std::endl; + for (int idx = 0; idx < pp_v[d].get_num_pairs(); ++idx) { + phat::index birth = pp_v[d].get_pair(idx).first; + phat::index death = pp_v[d].get_pair(idx).second; + std::cout << "(" << birth << ", " << death << ")" << std::endl; + } + } +} + +// XXX: Crashes if the file is not in the right format +void load(std::string prefix, std::vector& v, std::map& L, int& mx_dim) { + std::ifstream fin(std::string(std::string("data/") + prefix + std::string(".cx")).c_str()); + int nr_lines; + int idx, dim, isL; + int iK, iL; + double time; + + mx_dim = 0; + + v.clear(); + fin >> nr_lines; + while (nr_lines-- > 0) { + fin >> idx >> time >> dim >> isL; + Simplex s(idx, time, dim, isL); + mx_dim = std::max(mx_dim, dim); // Keep track of the highest-dimensional simplex + if (dim != 0 ) { + while (dim+1 > 0) { fin >> idx; --dim; s.add_bd(idx); } + } + v.push_back(s); + } + fin.close(); + + L.clear(); + fin.open(std::string(std::string("data/") + prefix + std::string(".L")).c_str()); + fin >> nr_lines; + while (nr_lines-- > 0) { + fin >> iL >> iK; + L[iL] = iK; + } + fin.close(); +} + +void experiments(std::string prefix) { + std::vector v; + std::map L; + int mx_dim; + std::cout << "Loading..." << std::endl; + load(prefix, v, L, mx_dim); + + std::vector pp_v(mx_dim+2); + std::cout << "Compuiting..." << std::endl; + compute_relative_persistence(v, L, pp_v); + std::cout << "Writing out..." << std::endl; + for (int d = 0; d < pp_v.size(); ++d) { + // std::cout << "Dimension " << d << std::endl; + std::string fname = std::string("data/")+prefix+std::string("_pp_")+std::to_string(d)+std::string(".pp"); + std::ofstream fout(fname.c_str()); + fout << pp_v[d].get_num_pairs() << std::endl; + for (int idx = 0; idx < pp_v[d].get_num_pairs(); ++idx) { + phat::index birth = pp_v[d].get_pair(idx).first; + phat::index death = pp_v[d].get_pair(idx).second; + double b_tm = v[birth].time(); + double d_tm = (death >= 0 ? v[death].time() : death); + fout << b_tm << " " << d_tm << std::endl; + // std::cout << "(" << b_tm << ", " << d_tm << ")" << std::endl; + } + fout.close(); + } +} + +void build_bdm(std::vector& v, std::map& L, phat::boundary_matrix< phat::vector_vector >& bd_m) { + const int N = v.size(); + + bd_m.set_num_cols(N); + + for (int idx = 0; idx < v.size(); ++idx) { + bd_m.set_dim(idx, v[idx].dim()); + } + + for (int idx = 0; idx < v.size(); ++idx) { + std::vector< phat::index > tmp_col; // tmp_col.clear(); + if (v[idx].dim() > 0) { + // The boudary consists of column indices---use label_to_idx for that + std::vector tmp_bd = v[idx].bd(); + for (int jdx = 0; jdx < tmp_bd.size(); ++jdx) { + tmp_col.push_back(tmp_bd[jdx]); + } + } + // If an L-simplex, add the corresponding K-simplex to its boundary + if (v[idx].inL()) { + // Need to find the corresponding K-simplex + const int idx_K = L.find(v[idx].idx())->second; + tmp_col.push_back(idx_K); + } + sort(tmp_col.begin(), tmp_col.end()); + bd_m.set_col(idx, tmp_col); + } +} + +void test() { + // first define a boundary matrix with the chosen internal representation + phat::boundary_matrix< phat::vector_vector > bd_m; + + // set the number of columns, which equals the number of simplices in the + // simplicial complex + bd_m.set_num_cols(19); + + // for each column, i, set the dimension of the simplex it represents + bd_m.set_dim(0, 0); // a_K + bd_m.set_dim(1, 0); // b_K + bd_m.set_dim(2, 0); // c_K + bd_m.set_dim(3, 0); // a_K + bd_m.set_dim(4, 0); // b_K + bd_m.set_dim(5, 0); // c_K + bd_m.set_dim(6, 0); // a_K + bd_m.set_dim(7, 0); // b_K + bd_m.set_dim(8, 0); // c_K + bd_m.set_dim(9, 0); // a_K + bd_m.set_dim(10, 0); // b_K + bd_m.set_dim(11, 0); // c_K + bd_m.set_dim(12, 0); // a_K + bd_m.set_dim(13, 0); // b_K + bd_m.set_dim(14, 0); // b_K + bd_m.set_dim(15, 0); // b_K + bd_m.set_dim(16, 0); // b_K + bd_m.set_dim(17, 1); // b_K + bd_m.set_dim(18, 1); // b_K + + std::vector< phat::index > tmp_col; + + // Define boundary of the 0-dimensional simplices, labeled with integers 0 through 2 + bd_m.set_col(0, tmp_col); + bd_m.set_col(1, tmp_col); + bd_m.set_col(2, tmp_col); + bd_m.set_col(3, tmp_col); + bd_m.set_col(4, tmp_col); + bd_m.set_col(5, tmp_col); + bd_m.set_col(6, tmp_col); + bd_m.set_col(7, tmp_col); + bd_m.set_col(8, tmp_col); + + // Now add the 2-dimensional simplex 3 whose boundary consists of 0-dimensional simplices 0 and 1 + tmp_col.push_back(6); + bd_m.set_col(9, tmp_col); + tmp_col.clear(); + + // Repeat for 1-dimensional simpliex 4 + tmp_col.push_back(7); + bd_m.set_col(10, tmp_col); + tmp_col.clear(); + + tmp_col.push_back(2); + bd_m.set_col(11, tmp_col); + tmp_col.clear(); + + tmp_col.push_back(0); + bd_m.set_col(12, tmp_col); + tmp_col.clear(); + + tmp_col.push_back(1); + bd_m.set_col(13, tmp_col); + tmp_col.clear(); + + tmp_col.push_back(3); + bd_m.set_col(14, tmp_col); + tmp_col.clear(); + + tmp_col.push_back(4); + bd_m.set_col(15, tmp_col); + tmp_col.clear(); + + tmp_col.push_back(8); + bd_m.set_col(16, tmp_col); + tmp_col.clear(); + + tmp_col.push_back(1); + tmp_col.push_back(6); + bd_m.set_col(17, tmp_col); + // tmp_col.clear(); + + tmp_col.push_back(17); + bd_m.set_col(18, tmp_col); + tmp_col.clear(); + + // Now compute the homology via matrix reduction + std::cout << "The boundary matrix has " << bd_m.get_num_cols() << " columns." << std::endl; + for (phat::index col_idx = 0; col_idx < bd_m.get_num_cols(); ++col_idx) { + std::cout << "Processing column corresponding to " << static_cast(bd_m.get_dim(col_idx)) << "-dimensional simplex." << std::endl; + if (!bd_m.is_empty(col_idx)) { // If column at the given index is not empty + std::vector tmp_col; + bd_m.get_col(col_idx, tmp_col); + std::cout << "Its boundary:" << std::endl; + for (phat::index idx = 0; idx < static_cast(tmp_col.size()); ++idx) { + std::cout << tmp_col[idx] << " "; + } + std::cout << std::endl; + } else { + std::cout << "Its boudary is the empty chain." << std::endl; + } + } + std::cout << "Overall, the matrix has " << bd_m.get_num_entries() << " entries." << std::endl; + + // Compute persistence + std::cout << "Persistence" << std::endl; + std::map L; L[13] = 1; L[12] = 0; L[11] = 2; L[10] = 7; L[9] = 6; L[14]=3; L[15]=4; L[16]=8; L[18]=17; + std::vector pp_v(2); + phat::compute_relative_persistence_pairs(pp_v, bd_m, L); + for (int d = 0; d < pp_v.size(); ++d) { + std::cout << "Dimension " << d << std::endl; + for (int idx = 0; idx < pp_v[d].get_num_pairs(); ++idx) { + phat::index birth = pp_v[d].get_pair(idx).first; + phat::index death = pp_v[d].get_pair(idx).second; + std::cout << "(" << birth << ", " << death << ")" << std::endl; + } + } +} + diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/src/self_test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/src/self_test.cpp new file mode 100644 index 0000000..249b9b2 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/src/self_test.cpp @@ -0,0 +1,225 @@ +/* Copyright 2013 IST Austria + Contributed by: Ulrich Bauer, Michael Kerber, Jan Reininghaus + + This file is part of PHAT. + + PHAT is free software: you can redistribute it and/or modify + it under the terms of the GNU Lesser General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + PHAT is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Lesser General Public License for more details. + + You should have received a copy of the GNU Lesser General Public License + along with PHAT. If not, see . */ + +#include + +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include + +int main( int argc, char** argv ) +{ + std::string test_data = argc > 1 ? argv[ 1 ] : "examples/torus.bin"; + + typedef phat::sparse_pivot_column Sparse; + typedef phat::heap_pivot_column Heap; + typedef phat::full_pivot_column Full; + typedef phat::bit_tree_pivot_column BitTree; + typedef phat::vector_vector Vec_vec; + typedef phat::vector_heap Vec_heap; + typedef phat::vector_set Vec_set; + typedef phat::vector_list Vec_list; + + std::cout << "Reading test data " << test_data << " in binary format ..." << std::endl; + phat::boundary_matrix< Full > boundary_matrix; + if( !boundary_matrix.load_binary( test_data ) ) { + std::cerr << "Error: test data " << test_data << " not found!" << std::endl; + return EXIT_FAILURE; + } + + bool error = false; + std::cout << "Comparing representations using Chunk algorithm ..." << std::endl; + { + std::cout << "Running Chunk - Sparse ..." << std::endl; + phat::persistence_pairs sparse_pairs; + phat::boundary_matrix< Sparse > sparse_boundary_matrix = boundary_matrix; + phat::compute_persistence_pairs< phat::chunk_reduction >( sparse_pairs, sparse_boundary_matrix ); + + std::cout << "Running Chunk - Heap ..." << std::endl; + phat::persistence_pairs heap_pairs; + phat::boundary_matrix< Heap > heap_boundary_matrix = boundary_matrix; + phat::compute_persistence_pairs< phat::chunk_reduction >( heap_pairs, heap_boundary_matrix ); + + std::cout << "Running Chunk - Full ..." << std::endl; + phat::persistence_pairs full_pairs; + phat::boundary_matrix< Full > full_boundary_matrix = boundary_matrix; + phat::compute_persistence_pairs< phat::chunk_reduction >( full_pairs, full_boundary_matrix ); + + std::cout << "Running Chunk - BitTree ..." << std::endl; + phat::persistence_pairs bit_tree_pairs; + phat::boundary_matrix< BitTree > bit_tree_boundary_matrix = boundary_matrix; + phat::compute_persistence_pairs< phat::chunk_reduction >( bit_tree_pairs, bit_tree_boundary_matrix ); + + std::cout << "Running Chunk - Vec_vec ..." << std::endl; + phat::persistence_pairs vec_vec_pairs; + phat::boundary_matrix< Vec_vec > vec_vec_boundary_matrix = boundary_matrix; + phat::compute_persistence_pairs< phat::chunk_reduction >( vec_vec_pairs, vec_vec_boundary_matrix ); + + std::cout << "Running Chunk - Vec_heap ..." << std::endl; + phat::persistence_pairs vec_heap_pairs; + phat::boundary_matrix< Vec_heap > vec_heap_boundary_matrix = boundary_matrix; + phat::compute_persistence_pairs< phat::chunk_reduction >( vec_heap_pairs, vec_heap_boundary_matrix ); + + std::cout << "Running Chunk - Vec_set ..." << std::endl; + phat::persistence_pairs vec_set_pairs; + phat::boundary_matrix< Vec_set > vec_set_boundary_matrix = boundary_matrix; + phat::compute_persistence_pairs< phat::chunk_reduction >( vec_set_pairs, vec_set_boundary_matrix ); + + std::cout << "Running Chunk - Vec_list ..." << std::endl; + phat::persistence_pairs vec_list_pairs; + phat::boundary_matrix< Vec_list > vec_list_boundary_matrix = boundary_matrix; + phat::compute_persistence_pairs< phat::chunk_reduction >( vec_list_pairs, vec_list_boundary_matrix ); + + if( sparse_pairs != heap_pairs ) { + std::cerr << "Error: sparse and heap differ!" << std::endl; + error = true; + } + if( heap_pairs != full_pairs ) { + std::cerr << "Error: heap and full differ!" << std::endl; + error = true; + } + if( full_pairs != vec_vec_pairs ) { + std::cerr << "Error: full and vec_vec differ!" << std::endl; + error = true; + } + if( vec_vec_pairs != vec_heap_pairs ) { + std::cerr << "Error: vec_vec and vec_heap differ!" << std::endl; + error = true; + } + if( vec_heap_pairs != vec_set_pairs ) { + std::cerr << "Error: vec_heap and vec_set differ!" << std::endl; + error = true; + } + if( vec_set_pairs != bit_tree_pairs ) { + std::cerr << "Error: vec_set and bit_tree differ!" << std::endl; + error = true; + } + if( bit_tree_pairs != vec_list_pairs ) { + std::cerr << "Error: bit_tree and vec_list differ!" << std::endl; + error = true; + } + if( vec_list_pairs != sparse_pairs ) { + std::cerr << "Error: vec_list and sparse differ!" << std::endl; + error = true; + } + + if( error ) return EXIT_FAILURE; + else std::cout << "All results are identical (as they should be)" << std::endl; + } + + std::cout << "Comparing algorithms using BitTree representation ..." << std::endl; + { + std::cout << "Running Twist - BitTree ..." << std::endl; + phat::persistence_pairs twist_pairs; + phat::boundary_matrix< BitTree > twist_boundary_matrix = boundary_matrix; + phat::compute_persistence_pairs< phat::twist_reduction >( twist_pairs, twist_boundary_matrix ); + + std::cout << "Running Standard - BitTree ..." << std::endl; + phat::persistence_pairs std_pairs; + phat::boundary_matrix< BitTree > std_boundary_matrix = boundary_matrix; + phat::compute_persistence_pairs< phat::standard_reduction >( std_pairs, std_boundary_matrix ); + + std::cout << "Running Chunk - BitTree ..." << std::endl; + phat::persistence_pairs chunk_pairs; + phat::boundary_matrix< BitTree > chunk_boundary_matrix = boundary_matrix; + phat::compute_persistence_pairs< phat::chunk_reduction >( chunk_pairs, chunk_boundary_matrix ); + + std::cout << "Running Row - BitTree ..." << std::endl; + phat::persistence_pairs row_pairs; + phat::boundary_matrix< BitTree > row_boundary_matrix = boundary_matrix; + phat::compute_persistence_pairs< phat::row_reduction >( row_pairs, row_boundary_matrix ); + + std::cout << "Running Spectral sequence - BitTree ..." << std::endl; + phat::persistence_pairs ss_pairs; + phat::boundary_matrix< BitTree > ss_boundary_matrix = boundary_matrix; + phat::compute_persistence_pairs< phat::spectral_sequence_reduction >( ss_pairs, ss_boundary_matrix ); + + if( twist_pairs != std_pairs ) { + std::cerr << "Error: twist and standard differ!" << std::endl; + error = true; + } + if( std_pairs != chunk_pairs ) { + std::cerr << "Error: standard and chunk differ!" << std::endl; + error = true; + } + if( chunk_pairs != row_pairs ) { + std::cerr << "Error: chunk and row differ!" << std::endl; + error = true; + } + if( row_pairs != ss_pairs ) { + std::cerr << "Error: row and spectral sequence differ!" << std::endl; + error = true; + } + if( ss_pairs != twist_pairs ) { + std::cerr << "Error: spectral sequence and twist differ!" << std::endl; + error = true; + } + + if( error ) return EXIT_FAILURE; + else std::cout << "All results are identical (as they should be)" << std::endl; + } + + std::cout << "Comparing primal and dual approach using Chunk - Full ..." << std::endl; + { + phat::persistence_pairs primal_pairs; + phat::boundary_matrix< Full > primal_boundary_matrix = boundary_matrix; + phat::compute_persistence_pairs< phat::chunk_reduction >( primal_pairs, primal_boundary_matrix ); + + phat::persistence_pairs dual_pairs; + phat::boundary_matrix< Full > dual_boundary_matrix = boundary_matrix; + phat::compute_persistence_pairs_dualized< phat::chunk_reduction >( dual_pairs, dual_boundary_matrix ); + + if( primal_pairs != dual_pairs ) { + std::cerr << "Error: primal and dual differ!" << std::endl; + error = true; + } + + if( error ) return EXIT_FAILURE; + else std::cout << "All results are identical (as they should be)" << std::endl; + } + + std::cout << "Testing vector interface ..." << std::endl; + { + std::vector< std::vector< int > > vector_vector_matrix; + std::vector< int > vector_dims; + boundary_matrix.save_vector_vector( vector_vector_matrix, vector_dims ); + phat::boundary_matrix< BitTree > vector_vector_boundary_matrix; + vector_vector_boundary_matrix.load_vector_vector( vector_vector_matrix, vector_dims ); + + if( vector_vector_boundary_matrix != boundary_matrix ) { + std::cerr << "Error: [load|save]_vector_vector bug" << std::endl; + error = true; + } + + if( error ) return EXIT_FAILURE; + else std::cout << "Test passed!" << std::endl; + } + + return EXIT_SUCCESS; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/src/simple_example.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/src/simple_example.cpp new file mode 100644 index 0000000..aff3ff9 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/phat/src/simple_example.cpp @@ -0,0 +1,127 @@ +/* Copyright 2013 IST Austria + Contributed by: Ulrich Bauer, Michael Kerber, Jan Reininghaus + + This file is part of PHAT. + + PHAT is free software: you can redistribute it and/or modify + it under the terms of the GNU Lesser General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + PHAT is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Lesser General Public License for more details. + + You should have received a copy of the GNU Lesser General Public License + along with PHAT. If not, see . */ + +// This file contains a simple example that demonstrates the usage of the library interface + +// wrapper algorithm that computes the persistence pairs of a given boundary matrix using a specified algorithm +#include + +// main data structure (choice affects performance) +#include + +// algorithm (choice affects performance) +#include +#include +#include +#include + +int main( int argc, char** argv ) +{ + std::cout << "We will build an ordered boundary matrix of this simplicial complex consisting of a single triangle: " << std::endl; + std::cout << std::endl; + std::cout << " 3" << std::endl; + std::cout << " |\\" << std::endl; + std::cout << " | \\" << std::endl; + std::cout << " | \\" << std::endl; + std::cout << " | \\ 4" << std::endl; + std::cout << "5| \\" << std::endl; + std::cout << " | \\" << std::endl; + std::cout << " | 6 \\" << std::endl; + std::cout << " | \\" << std::endl; + std::cout << " |________\\" << std::endl; + std::cout << " 0 2 1" << std::endl; + + + // first define a boundary matrix with the chosen internal representation + phat::boundary_matrix< phat::vector_vector > boundary_matrix; + + // set the number of columns (has to be 7 since we have 7 simplices) + boundary_matrix.set_num_cols( 7 ); + + // set the dimension of the cell that a column represents: + boundary_matrix.set_dim( 0, 0 ); + boundary_matrix.set_dim( 1, 0 ); + boundary_matrix.set_dim( 2, 1 ); + boundary_matrix.set_dim( 3, 0 ); + boundary_matrix.set_dim( 4, 1 ); + boundary_matrix.set_dim( 5, 1 ); + boundary_matrix.set_dim( 6, 2 ); + + // set the respective columns -- the columns entries have to be sorted + std::vector< phat::index > temp_col; + + boundary_matrix.set_col( 0, temp_col ); + + boundary_matrix.set_col( 1, temp_col ); + + temp_col.push_back( 0 ); + temp_col.push_back( 1 ); + boundary_matrix.set_col( 2, temp_col ); + temp_col.clear(); + + boundary_matrix.set_col( 3, temp_col ); + + temp_col.push_back( 1 ); + temp_col.push_back( 3 ); + boundary_matrix.set_col( 4, temp_col ); + temp_col.clear(); + + temp_col.push_back( 0 ); + temp_col.push_back( 3 ); + boundary_matrix.set_col( 5, temp_col ); + temp_col.clear(); + + temp_col.push_back( 2 ); + temp_col.push_back( 4 ); + temp_col.push_back( 5 ); + boundary_matrix.set_col( 6, temp_col ); + temp_col.clear(); + + // print some information of the boundary matrix: + std::cout << std::endl; + std::cout << "The boundary matrix has " << boundary_matrix.get_num_cols() << " columns: " << std::endl; + for( phat::index col_idx = 0; col_idx < boundary_matrix.get_num_cols(); col_idx++ ) { + std::cout << "Colum " << col_idx << " represents a cell of dimension " << (int)boundary_matrix.get_dim( col_idx ) << ". "; + if( !boundary_matrix.is_empty( col_idx ) ) { + std::vector< phat::index > temp_col; + boundary_matrix.get_col( col_idx, temp_col ); + std::cout << "Its boundary consists of the cells"; + for( phat::index idx = 0; idx < (phat::index)temp_col.size(); idx++ ) + std::cout << " " << temp_col[ idx ]; + } + std::cout << std::endl; + } + std::cout << "Overall, the boundary matrix has " << boundary_matrix.get_num_entries() << " entries." << std::endl; + + + // define the object to hold the resulting persistence pairs + phat::persistence_pairs pairs; + + // choose an algorithm (choice affects performance) and compute the persistence pair + // (modifies boundary_matrix) + phat::compute_persistence_pairs< phat::twist_reduction >( pairs, boundary_matrix ); + + // sort the persistence pairs by birth index + pairs.sort(); + + // print the pairs: + std::cout << std::endl; + std::cout << "There are " << pairs.get_num_pairs() << " persistence pairs: " << std::endl; + for( phat::index idx = 0; idx < pairs.get_num_pairs(); idx++ ) + std::cout << "Birth: " << pairs.get_pair( idx ).first << ", Death: " << pairs.get_pair( idx ).second << std::endl; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/py_bindings.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/py_bindings.cpp new file mode 100644 index 0000000..b1f2836 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/py_bindings.cpp @@ -0,0 +1,68 @@ +#ifndef PROFILE //TODO remove this? + +#include + +//#include "calc_pers_cuda.cuh" +//#include "vr_comp_cuda.cuh" +#include "vertex_filtration_comp_cuda.h" + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) +{ +// m.def("CalcPersCuda__find_merge_pairings", &CalcPersCuda::find_merge_pairings, "find_merge_pairings (CUDA)"); +// m.def("CalcPersCuda__merge_columns", &CalcPersCuda::merge_columns, "merge_columns (CUDA)"); +// m.def("CalcPersCuda__read_barcodes", &CalcPersCuda::read_barcodes, "read_barcodes (CUDA)"); +// m.def("CalcPersCuda__calculate_persistence", &CalcPersCuda::calculate_persistence, "calculate_persistence (CUDA)"); + +// m.def("VRCompCuda__vr_persistence", &VRCompCuda::vr_persistence, ""); +// m.def("VRCompCuda__vr_persistence_l1", &VRCompCuda::vr_persistence_l1, ""); +// m.def("VRCompCuda__write_combinations_table_to_tensor", &VRCompCuda::write_combinations_table_to_tensor, ""), +// m.def("VRCompCuda__co_faces_from_combinations", &VRCompCuda::co_faces_from_combinations, ""); + +// m.def("VRCompCuda__l1_norm_distance_matrix", &VRCompCuda::l1_norm_distance_matrix, ""); +// m.def("VRCompCuda__l2_norm_distance_matrix", &VRCompCuda::l2_norm_distance_matrix, ""); + +// pybind11::class_(m, "VRCompCuda__VietorisRipsArgsGenerator") +// .def(pybind11::init<>()) +// .def_readwrite("boundary_info_non_vertices", &VRCompCuda::VietorisRipsArgsGenerator::boundary_info_non_vertices) +// .def_readwrite("filtration_values_by_dim", &VRCompCuda::VietorisRipsArgsGenerator::filtration_values_by_dim) +// .def_readwrite("n_simplices_by_dim", &VRCompCuda::VietorisRipsArgsGenerator::n_simplices_by_dim) +// +// .def_readwrite("simplex_dimension_vector", &VRCompCuda::VietorisRipsArgsGenerator::simplex_dimension_vector) +// .def_readwrite("filtration_values_vector_without_vertices", &VRCompCuda::VietorisRipsArgsGenerator::filtration_values_vector_without_vertices) +// .def_readwrite("filtration_add_eps_hack_values", &VRCompCuda::VietorisRipsArgsGenerator::filtration_add_eps_hack_values) +// +// .def_readwrite("sort_indices_without_vertices", &VRCompCuda::VietorisRipsArgsGenerator::sort_indices_without_vertices) +// .def_readwrite("sort_indices_without_vertices_inverse", &VRCompCuda::VietorisRipsArgsGenerator::sort_indices_without_vertices_inverse) +// +// .def_readwrite("sorted_filtration_values_vector", &VRCompCuda::VietorisRipsArgsGenerator::sorted_filtration_values_vector) +// +// .def_readwrite("boundary_array", &VRCompCuda::VietorisRipsArgsGenerator::boundary_array) +// .def_readwrite("ba_row_i_to_bm_col_i_vector", &VRCompCuda::VietorisRipsArgsGenerator::ba_row_i_to_bm_col_i_vector) + +// .def("__call__", &VRCompCuda::VietorisRipsArgsGenerator::operator()) +// +// .def("init_state", &VRCompCuda::VietorisRipsArgsGenerator::init_state, "") +// .def("make_boundary_info_edges", &VRCompCuda::VietorisRipsArgsGenerator::make_boundary_info_edges, "") +// .def("make_boundary_info_non_edges", &VRCompCuda::VietorisRipsArgsGenerator::make_boundary_info_non_edges, "") +// .def("make_simplex_ids_compatible_within_dimensions", &VRCompCuda::VietorisRipsArgsGenerator::make_simplex_ids_compatible_within_dimensions, "") +// .def("make_simplex_dimension_vector", &VRCompCuda::VietorisRipsArgsGenerator::make_simplex_dimension_vector, "") +// .def("make_filtration_values_vector_without_vertices", &VRCompCuda::VietorisRipsArgsGenerator::make_filtration_values_vector_without_vertices, "") +// .def("do_filtration_add_eps_hack", &VRCompCuda::VietorisRipsArgsGenerator::do_filtration_add_eps_hack, "") +// .def("make_sorting_infrastructure", &VRCompCuda::VietorisRipsArgsGenerator::make_sorting_infrastructure, "") +// .def("undo_filtration_add_eps_hack", &VRCompCuda::VietorisRipsArgsGenerator::undo_filtration_add_eps_hack, "") +// .def("make_sorted_filtration_values_vector", &VRCompCuda::VietorisRipsArgsGenerator::make_sorted_filtration_values_vector, "") +// .def("make_boundary_array_rows_unsorted", &VRCompCuda::VietorisRipsArgsGenerator::make_boundary_array_rows_unsorted, "") +// .def("apply_sorting_to_rows", &VRCompCuda::VietorisRipsArgsGenerator::apply_sorting_to_rows, "") +// .def("make_ba_row_i_to_bm_col_i_vector", &VRCompCuda::VietorisRipsArgsGenerator::make_ba_row_i_to_bm_col_i_vector, "") + // .def("", &VRCompCuda::VietorisRipsArgsGenerator::, "") + ; + +// m.def("VertFiltCompCuda__vert_filt_comp_calculate_persistence_args", &VertFiltCompCuda::vert_filt_comp_calculate_persistence_args, "compute args for calculate_persistence from simplicial complex definition(CUDA)"); +// m.def("VertFiltCompCuda__vert_filt_persistence_single", &VertFiltCompCuda::vert_filt_persistence_single, ""); +// m.def("VertFiltCompCuda__vert_filt_persistence_batch", &VertFiltCompCuda::vert_filt_persistence_batch, ""); + m.def("VertExtendedFiltCompCuda_link_cut_tree__extended_persistence_batch", &VertExtendedFiltCompCuda_link_cut_tree::extended_filt_persistence_batch, ""); +// m.def("VertExtendedFiltCompCuda_boundary_matrix__vert_filt_extended_persistence_batch", &VertExtendedFiltCompCuda_boundary_matrix::extended_filt_persistence_batch, ""); + +} + +#endif \ No newline at end of file diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/tensor_utils.cu b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/tensor_utils.cu new file mode 100644 index 0000000..4c72cec --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/tensor_utils.cu @@ -0,0 +1,51 @@ +#include +#include + +#include "cuda_checks.cuh" +#include "param_checks_cuda.cuh" + + +using namespace at; + + +namespace TensorUtils{ + +namespace { + +template +__global__ void fill_range_kernel(scalar_t* out, int64_t out_numel){ + auto index = blockIdx.x * blockDim.x + threadIdx.x; + + if (index < out_numel){ + out[index] = index; + } +} +} + +void fill_range_cuda_(Tensor t) +{ + CHECK_CUDA(t); + at::OptionalDeviceGuard guard(device_of(t)); + + const int threads_per_block = 256; + const int blocks = t.numel()/threads_per_block + 1; + + auto scalar_type = t.type().scalarType(); + switch(scalar_type) + { + case ScalarType::Int: + fill_range_kernel<<>>(t.data(), t.numel()); + break; + + case ScalarType::Long: + fill_range_kernel<<>>(t.data(), t.numel()); + break; + + default: + throw std::invalid_argument("Unrecognized Type!"); + } + + cudaCheckError(); +} + +} // namespace TensorUtils \ No newline at end of file diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/tensor_utils.cuh b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/tensor_utils.cuh new file mode 100644 index 0000000..f5510d1 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/tensor_utils.cuh @@ -0,0 +1,12 @@ +#pragma once + + +#include + + +using namespace torch; + + +namespace TensorUtils{ + void fill_range_cuda_(Tensor t); +} \ No newline at end of file diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/binary_search.aoj.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/binary_search.aoj.test.cpp new file mode 100644 index 0000000..61abf74 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/binary_search.aoj.test.cpp @@ -0,0 +1,36 @@ +#define PROBLEM "http://judge.u-aizu.ac.jp/onlinejudge/description.jsp?id=ALDS1_4_B" +#include "../utils/binary_search.hpp" +#include "../utils/binary_search_max.hpp" + +#include +#include +#include +#include +#include "../utils/macros.hpp" +using namespace std; + +int main() { + int n; cin >> n; + vector s(n); + REP (i, n) { + cin >> s[i]; + } + + int q; cin >> q; + int64_t cnt = 0; + while (q --) { + int t_i; cin >> t_i; + int l = binsearch(0, n, [&](int i) { + return s[i] >= t_i; + }); + cnt += (l < n and s[l] == t_i); + + int r = binsearch_max(0, n, [&](int i) { + return s[i] <= t_i; + }); + assert ((l < n and s[l] == t_i) == (l <= r)); + } + + cout << cnt << endl; + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/binary_search.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/binary_search.hpp new file mode 100644 index 0000000..38e9c84 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/binary_search.hpp @@ -0,0 +1,19 @@ +#pragma once +#include +#include + +/** + * @brief a binary search / 二分探索 + * @param[in] p a monotone predicate defined on $[l, r)$ + * @return $\min \lbrace x \in [l, r) \mid p(x) \rbrace$, or r if it doesn't exist + */ +template +int64_t binsearch(int64_t l, int64_t r, UnaryPredicate p) { + assert (l <= r); + -- l; + while (r - l > 1) { + int64_t m = l + (r - l) / 2; + (p(m) ? r : l) = m; + } + return r; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/binary_search_float.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/binary_search_float.hpp new file mode 100644 index 0000000..3e41fad --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/binary_search_float.hpp @@ -0,0 +1,16 @@ +#pragma once +#include +#include "../utils/macros.hpp" + +/** + * @brief a binary search on floating point numbers / 二分探索 (浮動小数点数) + */ +template +double binsearch_float(double l, double r, UnaryPredicate p) { + assert (l <= r); + REP (i, Iteration) { + double m = (l + r) / 2; + (p(m) ? r : l) = m; + } + return r; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/binary_search_max.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/binary_search_max.hpp new file mode 100644 index 0000000..36c5d0e --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/binary_search_max.hpp @@ -0,0 +1,17 @@ +#pragma once +#include +#include + +/** + * @return $\max \lbrace x \in (l, r] \mid p(x) \rbrace$, or l if it doesn't exist + */ +template +int64_t binsearch_max(int64_t l, int64_t r, UnaryPredicate p) { + assert (l <= r); + ++ r; + while (r - l > 1) { + int64_t m = l + (r - l) / 2; + (p(m) ? l : r) = m; + } + return l; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/coordinate_compression.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/coordinate_compression.hpp new file mode 100644 index 0000000..3fe4332 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/coordinate_compression.hpp @@ -0,0 +1,26 @@ +#pragma once +#include +#include +#include "../utils/macros.hpp" + +template +struct coordinate_compression { + std::vector data; + coordinate_compression() = default; + template + coordinate_compression(Iterator first, Iterator last) { + unsafe_insert(first, last); + unsafe_rebuild(); + } + template + void unsafe_insert(Iterator first, Iterator last) { + data.insert(data.end(), first, last); + } + void unsafe_rebuild() { + std::sort(ALL(data)); + data.erase(std::unique(ALL(data)), data.end()); + } + int operator [] (const T & value) { + return std::lower_bound(ALL(data), value) - data.begin(); + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/dsu_on_tree.aoj.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/dsu_on_tree.aoj.test.cpp new file mode 100644 index 0000000..b6c4f65 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/dsu_on_tree.aoj.test.cpp @@ -0,0 +1,90 @@ +#define PROBLEM "http://judge.u-aizu.ac.jp/onlinejudge/description.jsp?id=2995" +#include "../utils/dsu_on_tree.hpp" +#include "../data_structure/union_find_tree_with_monoid.hpp" +#include "../monoids/plus.hpp" +#include "../hack/stack_pivot.hpp" + +#include +#include +#include +#include "../utils/macros.hpp" +using namespace std; + +vector solve(int n, int k, const vector > & g, const vector & c, const vector & d) { + union_find_tree_with_monoid > uft(k); + unordered_set used; + int value = 0; + auto func = [&](int e) { + return min(uft.tree_size(e), uft.get_value(e)); + }; + + auto add = [&](int i) { + int c_i = uft.find_root(c[i]); + int d_i = uft.find_root(d[i]); + if (c_i == d_i) { + value -= func(c_i); + uft.set_value(c_i, uft.get_value(c_i) + 1); + value += func(c_i); + } else { + value -= func(c_i); + value -= func(d_i); + uft.unite_trees(c_i, d_i); + if (not uft.is_root(c_i)) { + swap(c_i, d_i); + } + uft.set_value(c_i, uft.get_value(c_i) + 1); + value += func(c_i); + } + used.insert(c_i); + used.insert(d_i); + }; + auto reset = [&](int i) { + if (used.empty()) return; + for (int e : used) { + uft.data[e] = -1; + uft.value[e] = 0; + } + used.clear(); + value = 0; + }; + + vector answer(n, -1); + auto callback = [&](int i) { + answer[i] = value; + }; + + constexpr int root = 0; + dsu_on_tree(g, root, add, reset, callback); + return answer; +} + +int moin() { + // input + int n, k; cin >> n >> k; + vector > g(n); + REP (i, n - 1) { + int u, v; cin >> u >> v; + -- u; + -- v; + g[u].push_back(v); + g[v].push_back(u); + } + vector c(n); + vector d(n); + REP (i, n) { + cin >> c[i] >> d[i]; + -- c[i]; + -- d[i]; + } + + // solve + auto answer = solve(n, k, g, c, d); + + // output + REP (i, n) { + cout << answer[i] << endl; + } + return 0; +} + +STACK_PIVOT_MAIN(moin) diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/dsu_on_tree.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/dsu_on_tree.hpp new file mode 100644 index 0000000..808aade --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/dsu_on_tree.hpp @@ -0,0 +1,71 @@ +#pragma once +#include +#include +#include +#include "../graph/subtree.hpp" +#include "../utils/macros.hpp" + +/** + * @brief DSU on tree (sack) + * @arg g is a tree + * @arg root + * @arg add is a function object which takes a index of a vertex + * @arg sub is a function object which takes a index of a vertex + * @arg callback is a function object which takes a index of a vertex + * @note for each x, add(x) and sub(x) are called O(log n) times + * @note O(n log n) if add, sub, and callback are O(1) + * @see https://codeforces.com/blog/entry/44351 + * @note sub(x) can be implemented as reset(), because sub(x) is called until it becomes empty after sub(x) is called once + */ +template +void dsu_on_tree(const std::vector > & g, int root, Add & add, Sub & sub, Callback & callback) { + auto info = prepare_subtree_info(g, root); + auto subtree_apply = [&](int x, auto & f) { + std::stack stk; + stk.push(x); + while (not stk.empty()) { + int y = stk.top(); + stk.pop(); + f(y); + for (int z : g[y]) if (z != info[y].parent) { + stk.push(z); + } + } + }; + std::function go = [&](int x, bool keep) { + // leaf + if (info[x].size == 1) { + add(x); + callback(x); + if (not keep) { + sub(x); + } + return; + } + // choose the heavy child + int z = *max_element(ALL(g[x]), [&](int y1, int y2) { + int size1 = (y1 == info[x].parent ? -1 : info[y1].size); + int size2 = (y2 == info[x].parent ? -1 : info[y2].size); + return size1 < size2; + }); + // go light + for (int y : g[x]) if (y != info[x].parent) { + if (y != z) { + go(y, false); + } + } + // go heavy + go(z, true); + for (int y : g[x]) if (y != info[x].parent) { + if (y != z) { + subtree_apply(y, add); + } + } + add(x); + callback(x); + if (not keep) { + subtree_apply(x, sub); + } + }; + go(root, false); +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/fast_zeta_transform.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/fast_zeta_transform.hpp new file mode 100644 index 0000000..7a14089 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/fast_zeta_transform.hpp @@ -0,0 +1,26 @@ +#pragma once +#include +#include +#include "../number/primes.hpp" +#include "../utils/macros.hpp" + +/** + * @brief upward fast zeta transform on primes + * @note $O(n \log n)$ (or, $O(n \log \log n)$ ???) + * @return $b_i = \sum _ {i \mid j} a_j$ + * @note $a_0, b_0$ means the greatest element + */ +template +std::vector upward_fast_zeta_transform_on_primes(std::vector a, const prepared_primes & primes, const CommutativeSemiring & mon = CommutativeSemiring()) { + assert (a.size() <= primes.size); + if (a.empty()) return a; + for (int64_t p : primes.primes) { + REP3R (x, 1, (a.size() - 1) / p + 1) { + a[x] = mon.mult(a[x], a[p * x]); + } + } + REP3 (x, 1, a.size()) { + a[x] = mon.mult(a[x], a[0]); + } + return a; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/interval_map.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/interval_map.hpp new file mode 100644 index 0000000..112c9c5 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/interval_map.hpp @@ -0,0 +1,117 @@ +#pragma once +#include +#include +#include +#include + +/** + * @brief map from disjoint intervals + */ +template +class interval_map { + std::map > data; // the function which from right terminals r_i to the pairs (l_i, value) of left terminals l_i of intervals [l_i, r_i) + +public: + /** + * @note O(\log q) amortized + * @note [l_i, r_i) + */ + void update_interval(int64_t l, int64_t r, T value) { + assert (l <= r); + if (l == r) return; + auto it = data.lower_bound(l); + if (it != data.end() and it->second.first < l) { + auto r2 = it->first; + auto [l2, value2] = it->second; + if (r < r2) { // included + if (value2 == value) { + return; + } else { + it->second.first = r; + data.emplace(l, std::make_pair(l2, value2)); + } + } else { + if (value2 == value) { + l = l2; + data.erase(it); + } else if (l < r2) { + data.erase(it); + data.emplace(l, std::make_pair(l2, value2)); + } + } + } + while (true) { + it = data.upper_bound(l); + if (it == data.end() or r < it->second.first) { + break; + } + auto r2 = it->first; + auto [l2, value2] = it->second; + assert (l <= l2); + if (r2 <= r) { + data.erase(it); + } else { + if (value2 == value) { + r = r2; + data.erase(it); + } else if (l2 < r) { + it->second.first = r; + } + break; + } + } + data.emplace(r, std::make_pair(l, value)); + } + + /** + * @note O(\log q) + */ + void remove_containing_interval(int64_t x) { + auto it = data.upper_bound(x); + if (it != data.end() and it->second <= x) { + data.erase(it); + } + } + + /** + * @note O(\log q) + */ + std::optional find_value(int64_t x) const { + auto it = data.upper_bound(x); + if (it != data.end() and it->second.first <= x) { + return std::make_optional(it->second.second); + } else { + return std::optional(); + } + } + + /** + * @note O(\log q) + */ + std::optional > find_containing_interval(int64_t x) const { + auto it = data.upper_bound(x); + if (it != data.end() and it->second.first <= x) { + return std::make_optional(std::make_tuple(it->second.first, it->first, it->second.second)); + } else { + return std::optional >(); + } + } + + /** + * @note O(k \log q) + * @note [l_i, r_i) + */ + std::vector > list_intersecting_interval(int64_t l, int64_t r) const { + assert (l <= r); + std::vector > result; + while (true) { + auto it = data.upper_bound(l); + if (it == data.end() or r <= it->second.first) { + break; + } + result.emplace_back(it->second.first, it->first, it->second.second); + l = it->first; + } + return result; + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/interval_set.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/interval_set.hpp new file mode 100644 index 0000000..f59eebe --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/interval_set.hpp @@ -0,0 +1,76 @@ +#pragma once +#include +#include +#include +#include + +/** + * @brief set of disjoint intervals + */ +class interval_set { + std::map data; // the function which from right terminals r_i to left terminals l_i of intervals [l_i, r_i) + +public: + /** + * @note O(\log q) amortized + * @note [l_i, r_i) + */ + void add_interval(int64_t l, int64_t r) { + assert (l <= r); + if (l == r) return; + while (true) { + auto it = data.lower_bound(l); + if (it == data.end() or r < it->second) { + break; + } + l = std::min(l, it->second); + r = std::max(r, it->first); + data.erase(it); + } + data.emplace(r, l); + } + + /** + * @note O(\log q) + */ + void remove_containing_interval(int64_t x) { + auto it = data.upper_bound(x); + if (it != data.end() and it->second <= x) { + data.erase(it); + } + } + + /** + * @note O(\log q) + */ + std::optional > find_containing_interval(int64_t x) const { + auto it = data.upper_bound(x); + if (it != data.end() and it->second <= x) { + return std::make_optional(std::make_pair(it->second, it->first)); + } else { + return std::optional >(); + } + } + + /** + * @note O(k \log q) + * @note [l_i, r_i) + */ + std::vector > list_intersecting_interval(int64_t l, int64_t r) const { + assert (l <= r); + std::vector > result; + while (true) { + auto it = data.upper_bound(l); + if (it == data.end() or r <= it->second) { + break; + } + result.emplace_back(it->second, it->first); + l = it->first; + } + return result; + } + + size_t size() const { + return data.size(); + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/left_to_right_maxima.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/left_to_right_maxima.hpp new file mode 100644 index 0000000..aa360cf --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/left_to_right_maxima.hpp @@ -0,0 +1,62 @@ +#pragma once +#include +#include +#include +#include +#include "../utils/macros.hpp" +#include "../data_structure/sparse_table.hpp" +#include "../monoids/min.hpp" + +/** + * @brief Length of Left-to-right Maxima (前処理 $O(n \log n)$ + $O(1)$) + * @description computes the lengths of the left-to-right maxima for the given interval + * @note the left-to-right maxima for a sequence $a$ means the subsubsequence of the elements $a_i$ which satisfy $\forall j \lt i. a_j \lt a_i$. + */ +class left_to_right_maxima { + std::vector depth; + sparse_table > table; + +public: + left_to_right_maxima() = default; + + int operator () (int l, int r) const { + assert (0 <= l and l <= r and r <= (int)depth.size()); + if (l == r) return 0; + return depth[l] - table.range_get(l, r) + 1; + } + +private: + left_to_right_maxima(const std::vector & depth_) + : depth(depth_), table(ALL(depth_)) { + } + +public: + /** + * @note this is just a constructor, but is needed to specify template arguments. + */ + template , class RandomAccessIterator> + static left_to_right_maxima construct(RandomAccessIterator first, RandomAccessIterator last, const Comparator & cmp = Comparator()) { + int n = std::distance(first, last); + + // make a forest + std::vector parent(n, -1); + std::stack stk; + REP (i, n) { + while (not stk.empty() and cmp(*(first + stk.top()), *(first + i))) { + parent[stk.top()] = i; + stk.pop(); + } + stk.push(i); + } + + // calculate depths + std::vector depth(n); + REP_R (i, n) { + if (parent[i] != -1) { + depth[i] = depth[parent[i]] + 1; + } + } + + return left_to_right_maxima(depth); + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/macros.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/macros.hpp new file mode 100644 index 0000000..14bd069 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/macros.hpp @@ -0,0 +1,6 @@ +#pragma once +#define REP(i, n) for (int i = 0; (i) < (int)(n); ++ (i)) +#define REP3(i, m, n) for (int i = (m); (i) < (int)(n); ++ (i)) +#define REP_R(i, n) for (int i = (int)(n) - 1; (i) >= 0; -- (i)) +#define REP3R(i, m, n) for (int i = (int)(n) - 1; (i) >= (int)(m); -- (i)) +#define ALL(x) std::begin(x), std::end(x) diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/mex.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/mex.hpp new file mode 100644 index 0000000..02c83f1 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/mex.hpp @@ -0,0 +1,21 @@ +#pragma once +#include + +/** + * @brief Minimum Excluded Value ($O(n)$ for a given sorted container) + * @sa https://en.wikipedia.org/wiki/Mex_(mathematics) + * @note xs must be weakly increasing + */ +template +int mex(InputIterator first, InputIterator last) { + int last_x = 0; // only for debug + int y = 0; + for (; first != last; ++ first) { + int x = *first; + assert (last_x <= x); + last_x = x; + if (y < x) break; + if (y == x) ++ y; + } + return y; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/mex_calculator.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/mex_calculator.hpp new file mode 100644 index 0000000..f16ad8f --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/mex_calculator.hpp @@ -0,0 +1,20 @@ +#pragma once +#include + +class mex_calculator { + uint64_t used; +public: + mex_calculator() + : used(0) { + } + void put(int x) { + assert (0 <= x and x < 64 - 1); + used |= 1ull << x; + } + int get() const { + return __builtin_ctzll(~ used); + } + void clear() { + used = 0; + } +}; diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/mo_algorithm.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/mo_algorithm.hpp new file mode 100644 index 0000000..c132f47 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/mo_algorithm.hpp @@ -0,0 +1,53 @@ +#pragma once +#include +#include +#include +#include +#include +#include + +// struct mo_struct { +// typedef int64_t value_type; +// typedef int64_t result_type; +// void add_right(int nr, value_type x_r) { +// } +// void add_left(int nl, value_type x_nl) { +// } +// void remove_right(int nr, value_type x_nr) { +// } +// void remove_left(int nl, value_type x_l) { +// } +// result_type query() { +// return 0; +// } +// }; + +template +std::vector run_mo_algorithm(Mo mo, const std::vector& values, const std::vector >& queries) { + int n = values.size(); + int m = queries.size(); + + // sort queries + int sq_n = std::sqrt(n); + std::vector order(m); + std::iota(ALL(order), 0); + std::sort(ALL(order), [&](int i, int j) { + int l_i, r_i; std::tie(l_i, r_i) = queries[i]; + int l_j, r_j; std::tie(l_j, r_j) = queries[j]; + return std::make_pair(l_i / sq_n, r_i) < std::make_pair(l_j / sq_n, r_j); + }); + + // compute queries + std::vector ans(m); + int l = 0; + int r = 0; + for (int j : order) { + int nl, nr; std::tie(nl, nr) = queries[j]; + for (; r < nr; ++ r) mo.add_right(r + 1, values[r]); + for (; nl < l; -- l) mo.add_left(l - 1, values[l - 1]); + for (; nr < r; -- r) mo.remove_right(r - 1, values[r - 1]); + for (; l < nl; ++ l) mo.remove_left(l + 1, values[l]); + ans[j] = mo.query(); + } + return ans; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/mo_algorithm.yuki1270.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/mo_algorithm.yuki1270.test.cpp new file mode 100644 index 0000000..4f4c367 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/mo_algorithm.yuki1270.test.cpp @@ -0,0 +1,154 @@ +#define PROBLEM "https://yukicoder.me/problems/no/1270" +#include +#include +#include +#include +#include +#include +#include "../utils/macros.hpp" +#include "../data_structure/segment_tree.hpp" +#include "../data_structure/lazy_propagation_segment_tree.hpp" +#include "../monoids/min.hpp" +#include "../monoids/plus.hpp" +#include "../monoids/plus_min_action.hpp" +#include "../utils/mo_algorithm.hpp" +using namespace std; + + +struct mo_struct { + typedef int64_t value_type; + typedef int64_t result_type; + +private: + typedef segment_tree > plus_segtree_type; + typedef lazy_propagation_segment_tree, plus_monoid, plus_min_action > plus_min_segtree_type; + + // input + int n; + const vector& a; + + // static + unordered_map compress; + int k; + vector dp_l; + vector dp_r; + + // dynamic + int l; + int r; + int64_t value_lr; + plus_segtree_type segtree_l; + plus_min_segtree_type segtree_m; + plus_segtree_type segtree_r; + +public: + mo_struct(const vector& a_) + : n(a_.size()) + , a(a_) { + // static compress + vector sorted_a = a; + sort(ALL(sorted_a)); + for (int64_t a_i : sorted_a) { + compress.emplace(a_i, compress.size()); + } + k = compress.size(); + + // static left + dp_l.resize(n + 1); + segtree_l = plus_segtree_type(k); + segtree_l.range_set(0, k, 0); + REP (i, n) { + dp_l[i + 1] = dp_l[i] + segtree_l.range_get(compress[a[i]] + 1, k); + segtree_l.point_set(compress[a[i]], segtree_l.point_get(compress[a[i]]) + 1); + } + segtree_l = plus_segtree_type(k); + segtree_l.range_set(0, k, 0); + + // static right + dp_r.resize(n + 1); + segtree_r = plus_segtree_type(k); + segtree_r.range_set(0, k, 0); + REP_R (i, n) { + dp_r[i] = dp_r[i + 1] + segtree_r.range_get(0, compress[a[i]]); + segtree_r.point_set(compress[a[i]], segtree_r.point_get(compress[a[i]]) + 1); + } + + // dynamic + l = 0; + r = 0; + value_lr = 0; + segtree_m = plus_min_segtree_type(k); + segtree_m.range_set(0, k, 0); + REP (i, n) { + segtree_m.range_apply(compress[a[i]] + 1, k, 1); + } + } + + void add_right(int nr, int64_t x_r) { + assert (nr == r + 1); + int y = compress[x_r]; + value_lr -= segtree_l.range_get(y + 1, k); + segtree_m.range_apply(y + 1, k, -1); + segtree_r.point_set(y, segtree_r.point_get(y) - 1); + r = nr; + } + + void add_left(int nl, int64_t x_nl) { + assert (nl == l - 1); + int y = compress[x_nl]; + value_lr -= segtree_r.range_get(0, y); + segtree_l.point_set(y, segtree_l.point_get(y) - 1); + segtree_m.range_apply(0, y, -1); + l = nl; + } + + void remove_right(int nr, int64_t x_nr) { + assert (nr == r - 1); + int y = compress[x_nr]; + value_lr += segtree_l.range_get(y + 1, k); + segtree_m.range_apply(y + 1, k, 1); + segtree_r.point_set(y, segtree_r.point_get(y) + 1); + r = nr; + } + + void remove_left(int nl, int64_t x_l) { + assert (nl == l + 1); + int y = compress[x_l]; + value_lr += segtree_r.range_get(0, y); + segtree_l.point_set(y, segtree_l.point_get(y) + 1); + segtree_m.range_apply(0, y, 1); + l = nl; + } + + int64_t query() { + int64_t ans = 0; + ans += dp_l[l]; // the inversion number in [0, l) + ans += dp_r[r]; // the inversion number in [r, n) + ans += value_lr; // the inversion number between [0, l) and [r, n) + ans += (int64_t)(r - l) * segtree_m.range_get(0, k); // the inversion number between [0, l) and [l, r) plus the inversion number between [l, r) and [r, n) + return ans; + } +}; + +vector solve(int n, int q, const vector& a, const vector >& queries) { + return run_mo_algorithm(mo_struct(a), a, queries); +} + +int main() { + int n, q; cin >> n >> q; + vector a(n); + REP (i, n) { + cin >> a[i]; + } + vector > queries(q); + REP (i, q) { + int l, r; cin >> l >> r; + -- l; + queries[i] = make_pair(l, r); + } + auto ans = solve(n, q, a, queries); + REP (i, q) { + cout << ans[i] << endl; + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/next_combination.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/next_combination.hpp new file mode 100644 index 0000000..609b674 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/next_combination.hpp @@ -0,0 +1,51 @@ +#pragma once +#include + +/** + * @note copied from the reference implementation of N2639 + * @see http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2639.pdf + */ +template +bool next_combination(BidirectionalIterator first1, BidirectionalIterator last1, BidirectionalIterator first2, BidirectionalIterator last2) { + if (first1 == last1 or first2 == last2) { + return false; + } + BidirectionalIterator m1 = last1; + BidirectionalIterator m2 = last2; + -- m2; + while (-- m1 != first1 and not (*m1 < *m2)) { + } + bool result = (m1 == first1) and not (*first1 < *m2); + if (! result ) { + while (first2 != m2 and not (*m1 < *first2)) { + ++ first2; + } + first1 = m1; + std::iter_swap(first1, first2); + ++ first1; + ++ first2; + } + if (first1 != last1 and first2 != last2) { + m1 = last1; + m2 = first2; + while (m1 != first1 and m2 != last2) { + std::iter_swap(-- m1, m2); + ++ m2; + } + std::reverse(first1, m1); + std::reverse(first1, last1); + std::reverse(m2, last2); + std::reverse(first2, last2); + } + return not result; +} + +template +bool next_combination(BidirectionalIterator first, BidirectionalIterator middle, BidirectionalIterator last) { + return next_combination(first, middle, middle, last); +} + +template +inline bool prev_combination(BidirectionalIterator first, BidirectionalIterator middle, BidirectionalIterator last) { + return next_combination(middle, last, first, middle); +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/two_satisfiability.hpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/two_satisfiability.hpp new file mode 100644 index 0000000..67d6adb --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/two_satisfiability.hpp @@ -0,0 +1,36 @@ +#pragma once +#include +#include +#include +#include "../graph/strongly_connected_components.hpp" + +/** + * @brief 2-SAT ($O(N)$) + * @param n is the number of variables + * @param cnf is a proposition in a conjunctive normal form. Each literal is expressed as number $x$ s.t. $1 \le \vert x \vert \le n$ + * @return a vector with the length $n$ if SAT. It's empty if UNSAT. + */ +std::vector compute_two_satisfiability(int n, const std::vector > & cnf) { + // make digraph + std::vector > g(2 * n); + auto index = [&](int x) { + assert (x != 0 and abs(x) <= n); + return x > 0 ? x - 1 : n - x - 1; + }; + for (auto it : cnf) { + int x, y; std::tie(x, y) = it; // x or y + g[index(- x)].push_back(index(y)); // not x implies y + g[index(- y)].push_back(index(x)); // not y implies x + } + + // do SCC + std::vector component = decompose_to_strongly_connected_components(g).second; + std::vector valuation(n); + REP3 (x, 1, n + 1) { + if (component[index(x)] == component[index(- x)]) { // x iff not x + return std::vector(); // unsat + } + valuation[x - 1] = component[index(x)] > component[index(- x)]; // use components which indices are large + } + return valuation; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/two_satisfiability.yosupo.test.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/two_satisfiability.yosupo.test.cpp new file mode 100644 index 0000000..8693ee1 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/utils/two_satisfiability.yosupo.test.cpp @@ -0,0 +1,46 @@ +#define PROBLEM "https://judge.yosupo.jp/problem/two_sat" +#include +#include +#include +#include "../utils/macros.hpp" +#include "../hack/fastio.hpp" +#include "../utils/two_satisfiability.hpp" +using namespace std; + +int main() { + // read the header of DIMACS format + char const_p = in(); + assert (const_p == 'p'); + string const_cnf = in(); + assert (const_cnf == "cnf"); + + // input + int n = in(); + int m = in(); + vector > cnf; + REP (i, m) { + int a = in(); + int b = in(); + cnf.emplace_back(a, b); + + int const_zero = in(); + assert (const_zero == 0); + } + + // solve + vector model = compute_two_satisfiability(n, cnf); + + // output + if (model.empty()) { + out("s UNSATISFIABLE\n"); + } else { + out("s SATISFIABLE\n"); + out('v'); + REP (i, n) { + out(' '); + out((model[i] ? 1 : -1) * (i + 1)); + } + out(" 0 \n"); + } + return 0; +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/vertex_filtration_comp_cuda.cpp b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/vertex_filtration_comp_cuda.cpp new file mode 100644 index 0000000..73ecf23 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/vertex_filtration_comp_cuda.cpp @@ -0,0 +1,322 @@ +#include +//#include "ATen/core/function_schema.h" +// #include + +// #include +// #include +// #include +// #include +// #include + +#include +#include +#include +#include +#include +#include +#include +#include + +#include "vertex_filtration_comp_cuda.h" + +//link cut tree: +#include +#include + +using namespace torch; +auto EPS= 1e-20; +namespace VertExtendedFiltCompCuda_link_cut_tree{ + + class union_find { + public: + union_find(int64_t n) { + this->count = n; + this->parent.resize(n); + for (auto i = 0; i < n; i++) { + this->parent[i] = i; + } + this->rank.resize(n, 0); + } + + int64_t uf_depth(int64_t x) { + return rank[x]; + } + + int64_t find(int64_t x) { + int64_t tmp = x; + while (parent[tmp] != tmp) { + parent[tmp] = parent[parent[tmp]]; + tmp = parent[tmp]; + } + return tmp; + } + + void link(int64_t x, int64_t y) { + x = find(x); + y = find(y); + if (x == y) { + return; + } + if (rank[x] < rank[y]) { + parent[x] = y; + } else if (rank[x] > rank[y]) { + parent[y] = x; + } else { + parent[y] = x; + rank[x] = rank[x] + 1; + } + count--; + } + + int64_t num_connected_component() const { + return this->count; + } + + + private: + int64_t count; + std::vector parent; + std::vector rank; + }; + + std::vector compute_pd0(const Tensor &vertex_filtration, + const std::vector &boundary_info) { + auto num_nodes = vertex_filtration.size(0); + union_find uf = union_find(num_nodes); + Tensor tensor_edges = boundary_info[0]; + Tensor edge_val = std::get<0>(torch::max(vertex_filtration.index({tensor_edges}), 1)); + Tensor sorted_edge_indices = edge_val.argsort(-1, false); + const Tensor sorted_edges = tensor_edges.index({sorted_edge_indices}); + edge_val = edge_val.index({sorted_edge_indices}); + auto num_edges = sorted_edges.size(0); + std::vector pd_0; + + for (auto i = 0; i < num_edges; i++) { + auto e = sorted_edges[i]; + auto e_val = edge_val[i]; + int64_t u = e[0].item(); + int64_t v = e[1].item(); + int64_t root_u = uf.find(u); + int64_t root_v = uf.find(v); + if (root_u == root_v) { + continue; + } + int64_t root = root_u; + int64_t merged = root_v; + if (vertex_filtration[root].item() > vertex_filtration[merged].item()) + std::swap(root, merged); + else if (std::abs(vertex_filtration[root].item() - vertex_filtration[merged].item()) < + EPS) { + if (root > merged) + std::swap(root, merged); + } + auto merged_val = vertex_filtration[merged]; +// std::cout << "M: " << merged_val.item() << " E: " << e_val.item()<< std::endl; + Tensor pd_pair = torch::stack({merged_val, e_val}); + pd_0.emplace_back(pd_pair); + uf.link(root, merged); + } + return pd_0; + + } + + std::vector > extended_filt_persistence_single(const Tensor &vertex_filtration, + const std::vector &boundary_info) { + + std::vector > pd; + auto num_nodes = vertex_filtration.size(0); + union_find uf = union_find(num_nodes); + link_cut_tree > lct(num_nodes); + for (auto i = 0; i < num_nodes; i++) { + lct.vertex_set(i, {vertex_filtration[i].item(), i}); + } + std::vector pos_edge_index; + std::vector pd_0_up = compute_pd0(vertex_filtration, boundary_info); + std::vector pd_0_down, pd_0_ext_plus, pd_1_ext; + Tensor tensor_edges = boundary_info[0]; + Tensor edge_val = std::get<0>(torch::min(vertex_filtration.index({tensor_edges}), 1)); + Tensor sorted_edge_indices = edge_val.argsort(-1, true); + const Tensor sorted_edges = tensor_edges.index({sorted_edge_indices}); + edge_val = edge_val.index({sorted_edge_indices}); + auto num_edges = sorted_edges.size(0); + for (auto i = 0; i < num_edges; i++) { + auto e = sorted_edges[i]; + auto e_val = edge_val[i]; + int64_t u = e[0].item(); + int64_t v = e[1].item(); + int64_t root_u = uf.find(u); + int64_t root_v = uf.find(v); + if (root_u == root_v) { +// std::cout<<"u and v form positive edge: "<() < vertex_filtration[merged].item()) + std::swap(root, merged); + else if (std::abs(vertex_filtration[root].item() - vertex_filtration[merged].item()) < + EPS) { + if (root < merged) + std::swap(root, merged); + } + auto merged_val = vertex_filtration[merged]; + Tensor pd_pair = torch::stack({merged_val, e_val}); + pd_0_down.emplace_back(pd_pair); + uf.link(root, merged); + } + + //count the min and max per connected component: this is pd_0_ext_plus + std::vector connected_components_min(num_nodes, -1); + std::vector connected_components_max(num_nodes, -1); + for (auto v = 0; v < num_nodes; v++) { + auto v_root = uf.find(v); + if (uf.uf_depth(v_root) > 0) { + if (connected_components_max[v_root] == -1 && connected_components_min[v_root] == -1) { + connected_components_min[v_root] = v; + connected_components_max[v_root] = v; + } else { + if (vertex_filtration[v].item() > + vertex_filtration[connected_components_max[v_root]].item()) { + connected_components_max[v_root] = v; + } + if (vertex_filtration[v].item() < + vertex_filtration[connected_components_min[v_root]].item()) { + connected_components_min[v_root] = v; + } + } + } + } + + for (auto v = 0; v < num_nodes; v++) { + auto min_i = connected_components_min[v]; + auto max_i = connected_components_max[v]; + if (min_i != -1 && max_i != -1) { + pd_0_ext_plus.emplace_back(torch::stack({vertex_filtration[min_i], vertex_filtration[max_i]})); + } + } + if(pos_edge_index.size()>0){ + for (auto ii: pos_edge_index) { + auto pos_edge = sorted_edges[ii]; + auto pos_edge_val = edge_val[ii]; + int64_t u = pos_edge[0].item(); + int64_t v = pos_edge[1].item(); + + //print_graph(lct, num_nodes); + auto lca = lct.get_lowest_common_ancestor(u, v); + assert(lca != -1); + auto p1 = lct.path_get(u, lca); + auto p2 = lct.path_get(v, lca); + + int64_t critical_vertex;//the maximum on the loop + int64_t deletion_vertex;//the node to delete whose parent is the critical vertex + int64_t r; + + if (p1.first > p2.first) { + critical_vertex = p1.second; + r = lct.get_root(v); + } else { + critical_vertex = p2.second; + r = lct.get_root(u); + } + deletion_vertex = critical_vertex; + + auto search_node = u; + bool find_child_of_critical = false; + while (search_node != critical_vertex && search_node != lca) { + auto paren = lct.get_parent(search_node); + if (paren == critical_vertex) { + deletion_vertex = search_node; + break; + } + search_node = paren; + } + + if (search_node == lca) { + search_node = v; + while (search_node != critical_vertex && search_node != lca) { + auto paren = lct.get_parent(search_node); + if (paren == critical_vertex) { + deletion_vertex = search_node; + break; + } + search_node = paren; + } + } + + // print_graph(lct, num_nodes); + lct.cut(deletion_vertex); + // print_graph(lct, num_nodes); + + + auto u_rank = uf.uf_depth(u);//depth of union find connected component is in the union find data structure + auto v_rank = uf.uf_depth(v);//depth of union find connected component is in the union find data structure + + if (u_rank < v_rank) { + lct.evert(u); + lct.link(u, v); + } else { + lct.evert(v); + lct.link(v, u); + } + + auto cut_edge_val = vertex_filtration[critical_vertex]; + //std::cout << "CE " << cut_edge_val.item() << " AE " << pos_edge_val.item() << std::endl; + auto pers_pair = torch::stack({cut_edge_val, pos_edge_val}); + pd_1_ext.push_back(pers_pair); + } + } + + if(pd_0_up.size()==0){ + pd_0_up.push_back(torch::tensor({1.0, 1.0})); + } + if(pd_0_down.size()==0){ + pd_0_down.push_back(torch::tensor({1.0, 1.0})); + } + if(pd_0_ext_plus.size()==0){ + pd_0_ext_plus.push_back(torch::tensor({1.0, 1.0})); + } + if(pd_1_ext.size()==0){ + pd_1_ext.push_back(torch::tensor({1.0, 1.0})); + } + pd.push_back(pd_0_up); + pd.push_back(pd_0_down); + pd.push_back(pd_0_ext_plus); + pd.push_back(pd_1_ext); + return pd; + + } + + std::vector >> + extended_filt_persistence_batch(const std::vector >> & batch) { + auto futures = std::vector < std::future < std::vector < std::vector < Tensor >> >> (); + for (auto &arg: batch) { + futures. + push_back( + async(std::launch::async, [=] { + return extended_filt_persistence_single( + std::get<0>(arg), + std::get<1>(arg) + ); + } + ) + ); + } + auto ret = std::vector < std::vector < std::vector < Tensor>>>(); + for (auto &fut: futures){ + ret.push_back(fut.get()); + } + return ret; + } +} \ No newline at end of file diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/vertex_filtration_comp_cuda.h b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/vertex_filtration_comp_cuda.h new file mode 100644 index 0000000..a903533 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/vertex_filtration_comp_cuda.h @@ -0,0 +1,12 @@ +#pragma once + + +#include + + +using namespace torch; + +namespace VertExtendedFiltCompCuda_link_cut_tree{ + std::vector >> + extended_filt_persistence_batch(const std::vector >>& batch); +} diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/vr_comp_cuda.cu b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/vr_comp_cuda.cu new file mode 100644 index 0000000..e9e2acd --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/vr_comp_cuda.cu @@ -0,0 +1,827 @@ +#include +#include + +#include +#include +#include +#include +#include + +#include +#include + +#include "param_checks_cuda.cuh" +#include "tensor_utils.cuh" +#include "calc_pers_cuda.cuh" +#include "vr_comp_cuda.cuh" +#include "cuda_checks.cuh" + + +using namespace torch; + + +namespace VRCompCuda { + + +#pragma region binomal_table + +__device__ int64_t binom_coeff(int64_t n, int64_t k){ + int64_t res = 1; + + if ( k > n) return 0; + + // Since C(n, k) = C(n, n-k) + if ( k > n - k ){ + k = n - k; + } + + // Calculate value of [n * (n-1) *---* (n-k+1)] / [k * (k-1) *----* 1] + for (int64_t i = 0; i < k; i++) + { + res *= (n-i); + res /= (i + 1); + } + + return res; +} + + +__global__ void binomial_table_kernel(int64_t* out, int64_t max_n, int64_t max_k){ + int64_t r = blockIdx.x*blockDim.x + threadIdx.x; + int64_t c = blockIdx.y*blockDim.y + threadIdx.y; + + if (r < max_k && c < max_n){ + + out[r*max_n + c] = binom_coeff(c, r + 1); + + } +} + + +/** + * @brief + * + * @param max_n + * @param max_k + * @param type + * @return Tensor [max_k, max_n] where return[i, j] = binom(j, i+1) + */ +Tensor binomial_table(int64_t max_n, int64_t max_k, const Device& device){ + + at::OptionalDeviceGuard guard(device); + + auto ret = torch::empty({max_k, max_n}, torch::dtype(torch::kInt64).device(device)); + + dim3 threads_per_block = dim3(8, 8); + dim3 num_blocks= dim3(max_k/threads_per_block.y + 1, max_n/threads_per_block.x + 1); + + + binomial_table_kernel<<>>( + ret.data(), + max_n, + max_k); + + cudaCheckError(); + + return ret; +} + +#pragma endregion + +#pragma region write combinations table to tensor + +int64_t binom_coeff_cpu(int64_t n, int64_t k){ + int64_t res = 1; + + if ( k > n) return 0; + + // Since C(n, k) = C(n, n-k) + if ( k > n - k ){ + k = n - k; + } + + // Calculate value of [n * (n-1) *---* (n-k+1)] / [k * (k-1) *----* 1] + for (int64_t i = 0; i < k; i++) + { + res *= (n-i); + res /= (i + 1); + } + + return res; +} + +// Unranking of a comination, c.f., https://en.wikipedia.org/wiki/Combinatorial_number_system +__device__ void unrank_combination( + int64_t* out, // writing to this, assert length(out) == r + const int64_t N, // rank of the combination + const int64_t max_n, // cobinations of elements < max_n + const int64_t r, // number of combinations + int64_t* const binom_table //cache of combinations, assert binom_table[i,j] == binom(j,i) + ){ + + int64_t* bt_row; + int64_t rest = N; + bool broken = false; + + for (int64_t i=0; i rest){ + rest = rest - bt_row[j]; + out[i] = j; + broken = true; + break; + } + } + + if (!broken) { + out[i] = max_n - 1; + rest = rest - bt_row[max_n -1]; + } + + } +} + + +// writes the next combination into out, e.g., out = [3, 2, 1] -> out = [4, 2, 1] +__device__ void next_combination(int64_t* out, int64_t r){ + + // If we have to increase not the first digit ... + for (int64_t i = 0; i < r; i++){ + if (out[r - i - 2] > out[r - i - 1] + 1){ + out[r - i - 1] += 1; + + // fill the following digits with the smallest ordered sequence ... + for (int64_t j=0; j < i; j++){ + out[r - j - 1] = j; + } + return; + } + } + + // If the first digit has to be increased ... + out[0] += 1; + // fill the following digits with the smallest ordered sequence ... + for (int64_t j=0; j < r - 1; j++){ + out[r - j - 1] = j; + } + +} + + +__global__ void write_combinations_to_tensor_kernel( + int64_t* out, + const int64_t out_row_offset, + const int64_t out_row_stride, + const int64_t additive_constant, // additive constant which is added to the digits of each combination + const int64_t max_n, + const int64_t r, + int64_t* binom_table, + const int64_t n_comb_by_thread, + const int64_t n_max_over_r){ + + int64_t thread_id = blockIdx.x*blockDim.x + threadIdx.x; + + + if (thread_id*n_comb_by_thread < binom_coeff(max_n, r)){ // TODO use parameter instead of binom_coeff call + int64_t* comb = new int64_t[r]; + unrank_combination(comb, thread_id*n_comb_by_thread, max_n, r, binom_table); + + for (int64_t i = 0; i < n_comb_by_thread; i++){ + + if (thread_id*n_comb_by_thread + i >= n_max_over_r) break; + + for (int64_t j = 0; j < r; j++){ + out[out_row_stride * (out_row_offset + thread_id*n_comb_by_thread + i ) + j] + = comb[j] + additive_constant; + } + + next_combination(comb, r); + } + + delete[] comb; + } + + __syncthreads(); + +} + +/* +Writes all combinations of {0, ... , max_n -1} of length r +to out in lexicographical order. +Example max_n = 4, r = 3, off_set = 1, additive_constant = 0 + + out -> out + +-1 -1 -1 -1 -1 -1 -1 -1 +-1 -1 -1 -1 2 1 0 -1 +-1 -1 -1 -1 3 1 0 -1 +-1 -1 -1 -1 3 2 0 -1 +-1 -1 -1 -1 3 2 1 -1 +-1 -1 -1 -1 -1 -1 -1 -1 +*/ +void write_combinations_table_to_tensor( + const Tensor& out, + const int64_t out_row_offset, // the + const int64_t additive_constant, // constant added to each digit of the combination + const int64_t max_n, + const int64_t r + ){ + + CHECK_SMALLER_EQ(r, max_n); + const int64_t n_max_over_r = binom_coeff_cpu(max_n, r); + + CHECK_SMALLER_EQ(0, out_row_offset); + CHECK_SMALLER_EQ(n_max_over_r + out_row_offset, out.size(0)); + CHECK_SMALLER_EQ(r, out.size(1)); + CHECK_EQUAL(out.ndimension(), 2); + + at::OptionalDeviceGuard guard(device_of(out)); + + const auto bt = binomial_table(max_n, r, out.device()); + const int n_comb_by_thread = 100; //TODO optimize + int threads_per_block = 64; //TODO optimize + + int blocks = n_max_over_r/(threads_per_block*n_comb_by_thread) + 1; + + write_combinations_to_tensor_kernel<<>>( + out.data(), + out_row_offset, + out.size(1), + additive_constant, + max_n, + r, + bt.data(), + n_comb_by_thread, + n_max_over_r + ); + + cudaCheckError(); +} + +#pragma endregion + + +Tensor l1_norm_distance_matrix(const Tensor& points){ + Tensor ret = points.unsqueeze(1).expand({points.size(0), points.size(0), points.size(1)}); + + return (ret.transpose(0, 1) - ret).abs().sum(2); +} + + +Tensor l2_norm_distance_matrix(const Tensor & points){ + auto x = points.unsqueeze(1).expand({points.size(0), points.size(0), points.size(1)}); + return (x.transpose(0,1) - x).pow(2).sum(2).sqrt(); +} + + +#pragma region co-faces from combinations + + +__global__ void mask_of_valid_co_faces_from_combinations_kernel( + int64_t* combinations, + int64_t combinations_size_0, + int64_t combinations_size_1, + int64_t* faces, + int64_t faces_size_1, + int64_t* out + ){ + + int64_t thread_id = blockIdx.x*blockDim.x + threadIdx.x; + + if (thread_id < combinations_size_0){ + + const int n_faces = combinations_size_1*(combinations_size_1-1); + int64_t* current_comb = combinations + thread_id*combinations_size_1; + int64_t* tmp = new int64_t[n_faces + 1]; + + tmp[0] = -1; + for (int i = 0; i < combinations_size_1; i++){ + for(int j = 0; j < faces_size_1; j++){ + tmp[(i*faces_size_1 + j) + 1] = + faces[current_comb[i]*faces_size_1 + j]; + } + } + + + thrust::device_ptr dptr_tmp(tmp); + thrust::sort(thrust::seq, dptr_tmp, dptr_tmp + n_faces+1); + + bool is_boundary = true; + for (int i = 1; i < n_faces+1; i += 2){ + is_boundary = is_boundary && (dptr_tmp[i] == (dptr_tmp[i+1])); + is_boundary = is_boundary && (dptr_tmp[i-1] < dptr_tmp[i]); + } + + out[thread_id] = is_boundary ? 1 : 0; + + delete[] tmp; + } +} + + +Tensor co_faces_from_combinations( + const Tensor & combinations, + const Tensor & faces + ){ + + CHECK_SAME_DEVICE(combinations, faces); + at::OptionalDeviceGuard guard(device_of(combinations)); + + auto mask = torch::empty( + {combinations.size(0)}, + torch::dtype(torch::kInt64).device(combinations.device())); + + mask.fill_(0); + + int threads_per_block = 64; //TODO optimize + int blocks = combinations.size(0)/threads_per_block + 1; + + mask_of_valid_co_faces_from_combinations_kernel<<>>( + combinations.data(), + combinations.size(0), + combinations.size(1), + faces.data(), + faces.size(1), + mask.data() + ); + + cudaCheckError(); + + auto indices = mask.nonzero().squeeze(); + + return combinations.index_select(0, indices); +} + + +#pragma endregion + + +void VietorisRipsArgsGenerator::init_state( + const Tensor & distance_matrix, + int64_t max_dimension, + double max_ball_diameter + ){ + CHECK_TENSOR_CUDA_CONTIGUOUS(distance_matrix); + CHECK_SMALLER_EQ(max_dimension + 1, distance_matrix.size(0)); + CHECK_SMALLER_EQ(0, max_ball_diameter); + CHECK_EQUAL(distance_matrix.size(0), distance_matrix.size(1)); + + this->tensopt_real = torch::TensorOptions() + .dtype(distance_matrix.dtype()) + .device(distance_matrix.device()); + + this ->tensopt_int = torch::TensorOptions() + .dtype(torch::kInt64) + .device(distance_matrix.device()); + + this->distance_matrix = distance_matrix; + this->max_dimension = max_dimension; + this->max_ball_diameter = max_ball_diameter; + + this->n_simplices_by_dim.push_back(distance_matrix.size(0)); + + this->filtration_values_by_dim.push_back( + torch::empty({distance_matrix.size(0)}, + this->tensopt_real) + .fill_(0) + ); +} + + +void VietorisRipsArgsGenerator::make_boundary_info_edges(){ + Tensor ba_dim_1, filt_val_vec_dim_1; + auto n_edges = binom_coeff_cpu(distance_matrix.size(0), 2); + + ba_dim_1 = torch::empty({n_edges, 2}, this->tensopt_int); + + write_combinations_table_to_tensor(ba_dim_1, 0, 0, distance_matrix.size(0)/*=max_n*/, 2/*=r*/); + + // building the vector containing the filtraiton values of the edges + // in the same order as they appear in ba_dim_1... + auto x_indices = ba_dim_1.slice(1, 0, 1).squeeze(); + auto y_indices = ba_dim_1.slice(1, 1, 2); + + // filling filtration vector with edge filtration values ... + filt_val_vec_dim_1 = this->distance_matrix.index_select(0, x_indices); + filt_val_vec_dim_1 = filt_val_vec_dim_1.gather(1, y_indices); + filt_val_vec_dim_1 = filt_val_vec_dim_1.squeeze(); // + + // reduce to edges with filtration value <= max_ball_diameter... + if (max_ball_diameter > 0){ + auto i_select = filt_val_vec_dim_1.le(this->max_ball_diameter).nonzero().squeeze(); //TODO check + if (i_select.numel() == 0){ + + ba_dim_1 = torch::empty({0}, ba_dim_1.options()); + + filt_val_vec_dim_1 = torch::empty({0}, filt_val_vec_dim_1.options()); + } + else{ + ba_dim_1 = ba_dim_1.index_select(0, i_select); + filt_val_vec_dim_1 = filt_val_vec_dim_1.index_select(0, i_select); + } + } + + this->boundary_info_non_vertices.push_back(ba_dim_1); + this->filtration_values_by_dim.push_back(filt_val_vec_dim_1); + this->n_simplices_by_dim.push_back(ba_dim_1.size(0)); +} + + +void VietorisRipsArgsGenerator::make_boundary_info_non_edges(){ + Tensor filt_vals_prev_dim; + int64_t n_dim_min_one_simplices; + Tensor new_boundary_info, new_filt_vals; + + for (int dim = 2; dim <= this->max_dimension; dim++){ + + filt_vals_prev_dim = this->filtration_values_by_dim.at(dim - 1); + n_dim_min_one_simplices = filt_vals_prev_dim.size(0); + + if (n_dim_min_one_simplices < dim + 1){ + // There are not enough dim-1 simplices ... + new_boundary_info = torch::empty({0, dim + 1}, this->tensopt_int); + + new_filt_vals = torch::empty({0}, this->tensopt_real); + } + else{ + // There are enough dim - 1 simplices ... + auto combinations = torch::empty( + {binom_coeff_cpu(n_dim_min_one_simplices, dim + 1), dim + 1}, + this->tensopt_int); + + // write combinations ... + write_combinations_table_to_tensor(combinations, 0, 0, n_dim_min_one_simplices, dim + 1); + + new_boundary_info = co_faces_from_combinations(combinations, this->boundary_info_non_vertices.at(dim - 2)); + + auto bi_cloned = new_boundary_info.clone(); // we have to clone here other wise auto-grad does not work! + new_filt_vals = filt_vals_prev_dim.expand({new_boundary_info.size(0), filt_vals_prev_dim.size(0)}); + + new_filt_vals = new_filt_vals.gather(1, bi_cloned); + new_filt_vals = std::get<0>(new_filt_vals.max(1)); + + // If we have just one simplex of the current dimension this + // condition avoids that new_filt_vals is squeezed to a 0-dim + // Tensor + if (new_filt_vals.ndimension() != 1){ + new_filt_vals = new_filt_vals.squeeze(); + } + } + this->n_simplices_by_dim.push_back(new_boundary_info.size(0)); + this->boundary_info_non_vertices.push_back(new_boundary_info); + this->filtration_values_by_dim.push_back(new_filt_vals); + + } +} + + +void VietorisRipsArgsGenerator::make_simplex_ids_compatible_within_dimensions(){ + + auto index_offset = this->n_simplices_by_dim.at(0); + int dim; + for (int i=1; i < this->boundary_info_non_vertices.size(); i++){ + + dim = i + 1; + auto boundary_info = this->boundary_info_non_vertices.at(i); + boundary_info.add_(index_offset); + + index_offset += this->n_simplices_by_dim.at(dim-1); + } +} + + +void VietorisRipsArgsGenerator::make_simplex_dimension_vector(){ + int64_t n_simplices = 0; + for (int i = 0; i < this->n_simplices_by_dim.size(); i++){ + n_simplices += this->n_simplices_by_dim.at(i); + } + + simplex_dimension_vector = torch::empty({n_simplices}, + this->tensopt_int); + + auto max_dimension = this->max_dimension; + + int64_t copy_offset = 0; + for (int i = 0; i <= (max_dimension == 0 ? 1 : max_dimension) ; i++){ + simplex_dimension_vector + .slice(0, copy_offset, copy_offset + this->n_simplices_by_dim.at(i)) + .fill_(i); + + copy_offset += n_simplices_by_dim.at(i); + } + + this->simplex_dimension_vector = simplex_dimension_vector; +} + + +void VietorisRipsArgsGenerator::make_filtration_values_vector_without_vertices(){ + + std::vector filt_values_non_vertex_simplices; + for (int i = 1; i < this->filtration_values_by_dim.size(); i++){ + + auto filt_vals = this->filtration_values_by_dim.at(i); + filt_values_non_vertex_simplices.push_back(filt_vals); + } + + this->filtration_values_vector_without_vertices = cat(filt_values_non_vertex_simplices, 0); +} + + +void VietorisRipsArgsGenerator::do_filtration_add_eps_hack(){ + /* + This is a dirty hack to ensure that simplices do not occour before their boundaries + in the filtration. As the filtration is raised to higher dimensional simplices by + taking the maxium of the involved edge filtration values and sorting does not guarantee + a specific ordering in case of equal values we are forced to ensure a well defined + filtration by adding an increasing epsilon to each dimension. Later this has to be + substracted again. + Example: f([1,2,3]) = max(f([1,2]), f([3,1]), f([2,3])) --> w.l.o.g. f([1,2,3]) == f([1,2]) + Hence we set f([1,2,3]) = f([1,2]) + epsilon + */ + if (this->max_dimension >= 2 && this->n_simplices_by_dim.at(2) > 0){ + + auto filt_add_hack_values = torch::empty( + {this->filtration_values_vector_without_vertices.size(0)}, + this->tensopt_real) + .fill_(0); + + // we take epsilon of float to ensure that it is well defined even if + // we decide to alter the floating point type of the filtration values + // realm + float add_const_base_value = 100 * std::numeric_limits::epsilon(); // multily with 100 to be save against rounding issues + auto copy_offset = this->n_simplices_by_dim.at(1); + + for (int dim = 2; dim <= max_dimension; dim++){ + filt_add_hack_values.slice(0, copy_offset, copy_offset + this->n_simplices_by_dim.at(dim)) + .fill_(add_const_base_value); + + add_const_base_value += add_const_base_value; + copy_offset += this->n_simplices_by_dim.at(dim); + } + + this->filtration_values_vector_without_vertices += filt_add_hack_values; + this->filtration_add_eps_hack_values = filt_add_hack_values; + } + +} + + +void VietorisRipsArgsGenerator::make_sorting_infrastructure(){ + auto sort_ret = this->filtration_values_vector_without_vertices.sort(0); + this->sort_indices_without_vertices = std::get<1>(sort_ret); + this->sort_indices_without_vertices_inverse = + std::get<1>(this->sort_indices_without_vertices.sort(0)); +} + + +void VietorisRipsArgsGenerator::undo_filtration_add_eps_hack(){ + if (this->max_dimension >= 2 && this->n_simplices_by_dim.at(2) > 0){ + this->filtration_values_vector_without_vertices -= + this->filtration_add_eps_hack_values; + } +} + + +void VietorisRipsArgsGenerator::make_sorted_filtration_values_vector(){ + + auto dim_0_filt_values = torch::empty({n_simplices_by_dim.at(0)}, this->tensopt_real); + + dim_0_filt_values.fill_(0); + + auto tmp = this->filtration_values_vector_without_vertices + .index_select(0, this->sort_indices_without_vertices); + + tmp = cat({dim_0_filt_values, tmp}); + + this->sorted_filtration_values_vector = tmp; +} + + +void VietorisRipsArgsGenerator::make_boundary_array_rows_unsorted(){ + auto n_non_vertex_simplices = 0; + for (int i=1; i < this->n_simplices_by_dim.size(); i++){ + n_non_vertex_simplices += this->n_simplices_by_dim.at(i); + } + + auto ba = torch::empty( + {n_non_vertex_simplices, (this->max_dimension == 0 ? 4 : (max_dimension + 1)*2)}, + this->tensopt_int); + ba.fill_(-1); + + // copy edges ... + auto edge_boundary_info = this->boundary_info_non_vertices.at(0); + ba.slice(0, 0, this->n_simplices_by_dim.at(1)).slice(1, 0, 2) + = edge_boundary_info; + + if (this->max_dimension >= 2){ + + auto look_up_table_row = this->sort_indices_without_vertices_inverse; + + // adapt to indexation with vertices ... + look_up_table_row += this->n_simplices_by_dim.at(0); + + // ensure length is according to indexation with vertices ... + auto dummy_vals = + torch::empty({n_simplices_by_dim.at(0)}, look_up_table_row.options()) + .fill_(std::numeric_limits::max()); + + look_up_table_row = cat({dummy_vals, look_up_table_row}, 0); + + int64_t copy_offset = this->n_simplices_by_dim.at(1); + + + for (int dim = 2; dim <= this->max_dimension; dim++){ + + auto boundary_info = this->boundary_info_non_vertices.at(dim-1); + + if (boundary_info.size(0) == 0){ + continue; + } + + auto look_up_table = look_up_table_row.expand( + {boundary_info.size(0), look_up_table_row.size(0)}); + + // Apply ordering to row content ... + boundary_info = look_up_table.gather(1, boundary_info); + + // Ensure row contents are descendingly ordered ... + boundary_info = std::get<0>(boundary_info.sort(1, /*descending=*/true)); + + ba.slice(0, copy_offset, copy_offset + boundary_info.size(0)) + .slice(1, 0, boundary_info.size(1)) + = boundary_info; + + copy_offset += boundary_info.size(0); + } + } + + this->boundary_array = ba; +} + + +void VietorisRipsArgsGenerator::apply_sorting_to_rows(){ + this->boundary_array = boundary_array.index_select( + 0, + this->sort_indices_without_vertices + ); + + auto simp_dim_slice = this->simplex_dimension_vector.slice(0, this->n_simplices_by_dim.at(0)); + + simp_dim_slice.copy_(simp_dim_slice.index_select(0, this->sort_indices_without_vertices)); +} + + +void VietorisRipsArgsGenerator::make_ba_row_i_to_bm_col_i_vector(){ + auto tmp = torch::empty({this->boundary_array.size(0)}, this->tensopt_int); + TensorUtils::fill_range_cuda_(tmp); + tmp += this->n_simplices_by_dim.at(0); + + this->ba_row_i_to_bm_col_i_vector = tmp; +} + +std::vector VietorisRipsArgsGenerator::operator()( + const Tensor & distance_matrix, + int64_t max_dimension, + double max_ball_diameter){ + + std::vector ret; + + this->init_state(distance_matrix, max_dimension, max_ball_diameter); + this->make_boundary_info_edges(); + + if (this->n_simplices_by_dim.at(1) > 0){ + this->make_boundary_info_non_edges(); + this->make_simplex_ids_compatible_within_dimensions(); + this->make_simplex_dimension_vector(); + this->make_filtration_values_vector_without_vertices(); + this->do_filtration_add_eps_hack(); + this->make_sorting_infrastructure(); + this->undo_filtration_add_eps_hack(); + this->make_sorted_filtration_values_vector(); + this->make_boundary_array_rows_unsorted(); + this->apply_sorting_to_rows(); + this->make_ba_row_i_to_bm_col_i_vector(); + + + ret.push_back(this->boundary_array); + ret.push_back(this->ba_row_i_to_bm_col_i_vector); + ret.push_back(this->simplex_dimension_vector); + ret.push_back(this->sorted_filtration_values_vector); + } + else + { + ret.push_back(torch::empty({0, 2*(max_dimension + 1)}, this->tensopt_int)); + ret.push_back(torch::empty({0}, this->tensopt_int)); + ret.push_back(torch::zeros({distance_matrix.size(0)}, this->tensopt_int)); + + // We generate the 0-vector in this way to ensure that distance_matrix + // will have zero gradients instead of None after a backward call + // in pytorch ... + auto filtration_values = distance_matrix.slice(1, 0, 1).squeeze().clone(); + filtration_values.fill_(0); + ret.push_back(filtration_values); + } + return ret; +} + + +std::vector> calculate_persistence_output_to_barcode_tensors( + const std::vector>& calculate_persistence_output, + const Tensor & filtration_values){ + std::vector> ret; + + std::vector non_essential_barcodes; + { + auto non_essentials = calculate_persistence_output.at(0); + Tensor birth_death_i, births, birth_i, deaths, death_i, barcodes, i_birth_ne_death; + for (int i = 0; i < non_essentials.size(); i++){ + + birth_death_i = non_essentials.at(i); + + if(birth_death_i.numel() == 0){ + barcodes = torch::empty({0, 2}, filtration_values.options()); + } + else { + birth_i = birth_death_i.slice(1, 0, 1).squeeze(); + births = filtration_values.index_select(0, birth_i); + + death_i = birth_death_i.slice(1, 1, 2).squeeze(); + deaths = filtration_values.index_select(0, death_i); + i_birth_ne_death = births.ne(deaths).nonzero().squeeze(); + + if (i_birth_ne_death.numel() != 0){ + births = births.index_select(0, i_birth_ne_death); + deaths = deaths.index_select(0, i_birth_ne_death); + barcodes = stack({births, deaths}, 1); + } + else{ + barcodes = torch::empty({0, 2}, filtration_values.options()); + } + + } + non_essential_barcodes.push_back(barcodes); + } + ret.push_back(non_essential_barcodes); + } + + std::vector essential_barcodes; + { + auto essentials = calculate_persistence_output.at(1); + Tensor birth_i, births, barcodes; + for (int i = 0; i < essentials.size(); i++){ + + birth_i = essentials.at(i).squeeze(); + + if (birth_i.numel() == 0){ + barcodes = torch::empty({0}, filtration_values.options()); + } + else { + barcodes = filtration_values.index_select(0, birth_i); + } + + essential_barcodes.push_back(barcodes); + } + + ret.push_back(essential_barcodes); + } + + return ret; +} + + +std::vector> vr_persistence( + const Tensor& distance_matrix, + int64_t max_dimension, + double max_ball_diameter){ + + std::vector> ret; + auto args_generator = VietorisRipsArgsGenerator(); + + auto args = args_generator(distance_matrix, max_dimension, max_ball_diameter); + + auto pers = CalcPersCuda::calculate_persistence( + args.at(0), args.at(1), args.at(2), max_dimension, -1 + ); + + auto filtration_values = args.at(3); + ret = calculate_persistence_output_to_barcode_tensors(pers, filtration_values); + + return ret; +} + + +std::vector> vr_persistence_l1( + const Tensor& point_cloud, + int64_t max_dimension, + double max_ball_diameter){ + + auto distance_matrix = l1_norm_distance_matrix(point_cloud); + + return vr_persistence(distance_matrix, max_dimension, max_ball_diameter); +} + + + +} // namespace VRCompCuda \ No newline at end of file diff --git a/torchPHext/torchex_PHext/pershom/pershom_cpp_src/vr_comp_cuda.cuh b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/vr_comp_cuda.cuh new file mode 100644 index 0000000..4640252 --- /dev/null +++ b/torchPHext/torchex_PHext/pershom/pershom_cpp_src/vr_comp_cuda.cuh @@ -0,0 +1,93 @@ +#pragma once + + +#include + + +using namespace torch; + + +namespace VRCompCuda { + std::vector> vr_persistence( + const Tensor& distance_matrix, + int64_t max_dimension, + double max_ball_diameter); + + std::vector> vr_persistence_l1( + const Tensor& point_cloud, + int64_t max_dimension, + double max_ball_diameter); + + void write_combinations_table_to_tensor( + const Tensor& out, + const int64_t out_row_offset, + const int64_t additive_constant, + const int64_t max_n, + const int64_t r); + + Tensor co_faces_from_combinations( + const Tensor & combinations, + const Tensor & faces); + + Tensor l1_norm_distance_matrix(const Tensor & points); + + Tensor l2_norm_distance_matrix(const Tensor & points); + + class VietorisRipsArgsGenerator + { + public: + + at::TensorOptions tensopt_real; + at::TensorOptions tensopt_int; + + Tensor distance_matrix; + int64_t max_dimension; + double max_ball_diameter; + + std::vector boundary_info_non_vertices; + std::vector filtration_values_by_dim; + std::vector n_simplices_by_dim; + + Tensor simplex_dimension_vector; + Tensor filtration_values_vector_without_vertices; + Tensor filtration_add_eps_hack_values; + + Tensor sort_indices_without_vertices; + Tensor sort_indices_without_vertices_inverse; + Tensor sorted_filtration_values_vector; + + Tensor boundary_array; + + Tensor ba_row_i_to_bm_col_i_vector; + + std::vector operator()( + const Tensor & distance_matrix, + int64_t max_dimension, + double max_ball_diameter); + + void init_state( + const Tensor & distance_matrix, + int64_t max_dimension, + double max_ball_diameter + ); + + void make_boundary_info_edges(); + void make_boundary_info_non_edges(); + void make_simplex_ids_compatible_within_dimensions(); + void make_simplex_dimension_vector(); + void make_filtration_values_vector_without_vertices(); + void do_filtration_add_eps_hack(); + void make_sorting_infrastructure(); + void undo_filtration_add_eps_hack(); + void make_sorted_filtration_values_vector(); + void make_boundary_array_rows_unsorted(); + void apply_sorting_to_rows(); + void make_ba_row_i_to_bm_col_i_vector(); + }; + + + std::vector> calculate_persistence_output_to_barcode_tensors( + const std::vector>& calculate_persistence_output, + const Tensor & filtration_values); +} + diff --git a/train/2cycles_train_eval.py b/train/2cycles_train_eval.py new file mode 100644 index 0000000..5c9556f --- /dev/null +++ b/train/2cycles_train_eval.py @@ -0,0 +1,288 @@ +import time +import sys + +import torch +import torch.nn.functional as F +from torch import tensor +from torch.optim import Adam +from sklearn.model_selection import StratifiedKFold +# from torch_geometric.loader import DataLoader, DenseDataLoader as DenseLoader +from torch.utils.data import DataLoader +from torch.utils.tensorboard import SummaryWriter +writer= SummaryWriter() + +import sys +sys.path.append(".") + +import os.path as osp +import uuid +import pickle +import datetime +import random +import torch +import os + +import argparse + +from torch.utils.tensorboard import SummaryWriter +writer= SummaryWriter() + +import numpy as np + +import torch.optim as optim + +from torch.optim.lr_scheduler import MultiStepLR + +torch.backends.cudnn.deterministic = True +# torch.manual_seed(12345) +# torch.cuda.manual_seed_all(12345) +# random.seed(12345) +# np.random.seed(12345) + +from gnn.twocycles_model import PershomLearnedFilt, PershomLearnedFiltSup, PershomRigidDegreeFilt, GIN, SimpleNNBaseline, ClassicGNN, ClassicReadoutFilt +from data.data import dataset_factory, train_test_val_split, Subset +from data.utils import my_collate + + +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + +class Subset(torch.utils.data.Dataset): + def __init__(self, dataset, indices): + assert isinstance(indices, (list, tuple)) + self.ds = dataset + self.indices = tuple(indices) + self.y = dataset.y[indices] + assert len(indices) <= len(dataset) + + def __len__(self): + return len(self.indices) + + def __getitem__(self, idx): + return self.ds[self.indices[idx]] + + +def cross_validation_with_val_set(args, dataset, model, folds, epochs, batch_size, + lr, lr_decay_factor, lr_decay_step_size, + weight_decay, logger=None): + + val_losses, accs, durations = [], [], [] + for fold, (train_idx, test_idx, + val_idx) in enumerate(zip(*k_fold(dataset, folds, args.device))): + tr_dataset = Subset(dataset, train_idx) + te_dataset = Subset(dataset, test_idx) + val_dataset = Subset(dataset, val_idx) + dl_train = torch.utils.data.DataLoader( + tr_dataset, + collate_fn=my_collate, + batch_size=args.batch_size, # make this BIG for better contrastive learning + shuffle=True, + # if last batch would have size 1 we have to drop it ... + drop_last=(len(tr_dataset) % args.batch_size == 1) + ) + + dl_val = torch.utils.data.DataLoader( + val_dataset, + collate_fn=my_collate, + batch_size=1, + shuffle=False, + # drop_last = (len(dataset) % 64 == 1) + ) + dl_test = torch.utils.data.DataLoader( + te_dataset, + collate_fn=my_collate, + batch_size=1, + shuffle=False, + # drop_last = (len(dataset) % 64 == 1) + ) + model.to(device).reset_parameters() + optimizer = Adam(model.parameters(), lr=lr, weight_decay=weight_decay) + + if torch.cuda.is_available(): + torch.cuda.synchronize() + + t_start = time.perf_counter() + + for epoch in range(1, epochs + 1): + train_loss = train_ph(args, model, optimizer, dl_train) + val_losses.append(eval_loss(model, dl_val)) + accs.append(eval_acc(model, dl_test)) + eval_info = { + 'fold': fold, + 'epoch': epoch, + 'train_loss': train_loss, + 'val_loss': val_losses[-1], + 'test_acc': accs[-1], + } + print(eval_info) + + writer.add_scalars('loss_' + args.exp_name, {'train_loss_fold'+str(fold): float(train_loss)}, epoch) + writer.add_scalars('acc_' + args.exp_name, {'test_acc_fold'+str(fold): float(accs[-1])}, epoch) + if logger is not None: + logger(eval_info) + + if epoch % lr_decay_step_size == 0: + for param_group in optimizer.param_groups: + param_group['lr'] = lr_decay_factor * param_group['lr'] + + if torch.cuda.is_available(): + torch.cuda.synchronize() + + t_end = time.perf_counter() + durations.append(t_end - t_start) + + loss, acc, duration = tensor(val_losses), tensor(accs), tensor(durations) + loss, acc = loss.view(folds, epochs), acc.view(folds, epochs) + loss, argmin = loss.min(dim=1) + acc = acc[torch.arange(folds, dtype=torch.long), argmin]### + loss_mean = loss.mean().item() + acc_mean = acc.mean().item() + acc_std = acc.std().item() + duration_mean = duration.mean().item() + print(f'Val Loss: {loss_mean:.4f}, Test Accuracy: {acc_mean:.3f} ' + f'± {acc_std:.3f}, Duration: {duration_mean:.3f}') + + return loss_mean, acc_mean, acc_std + + +def k_fold(dataset, folds, device): + skf = StratifiedKFold(folds, shuffle=True, random_state=12345) + + test_indices, train_indices = [], [] + y= torch.tensor([dataset[i].y.item() for i in range(len(dataset))], dtype= torch.long) + + for _, idx in skf.split(torch.zeros(len(dataset)), y): + test_indices.append(list(idx)) + + val_indices = [test_indices[i - 1] for i in range(folds)] + + for i in range(folds): + train_mask = torch.ones(len(dataset), dtype=torch.bool) + train_mask[test_indices[i]] = 0 + train_mask[val_indices[i]] = 0 + train_indices.append(list(train_mask.nonzero(as_tuple=False).view(-1).cpu().numpy())) + return (list(train_indices)), (list(test_indices)), (list(val_indices)) + +def num_graphs(data): + if hasattr(data, 'num_graphs'): + return data.num_graphs + else: + return data.x.size(0) + +def train_ph(args, model, opt, dl_train): + model.train() + + total_loss = 0 + + for batch_i, batch in enumerate(dl_train, start=1): + opt.zero_grad() + batch = batch.to(device) + if not hasattr(batch, 'node_lab'): batch.node_lab = None + batch.boundary_info = [e.to(device) for e in batch.boundary_info] + logit= (model(batch,device)).to(device) + loss1 = F.nll_loss(logit.float(), batch.y.view(-1).long()) + loss= loss1 + loss.backward() + total_loss += loss.item() * num_graphs(batch) + opt.step() + return total_loss / len(dl_train.dataset) + +def train(model, optimizer, loader): + model.train() + + total_loss = 0 + for data in loader: + optimizer.zero_grad() + data = data.to(device) + out = model(data, device) + loss = F.nll_loss(out.float(), data.y.view(-1).long()) + loss.backward() + total_loss += loss.item() * num_graphs(data) + optimizer.step() + return total_loss / len(loader.dataset) + + +def eval_acc(model, loader): + model.eval() + + correct = 0 + for data in loader: + data = data.to(device) + with torch.no_grad(): + pred = model(data, device).max(1)[1] + correct += pred.eq(data.y.view(-1)).sum().item() + return correct / len(loader.dataset) + + +def eval_loss(model, loader): + model.eval() + + loss = 0 + for data in loader: + data = data.to(device) + with torch.no_grad(): + out = model(data, device) + loss += F.nll_loss(out.float(), data.y.view(-1).long(), reduction='sum').item() + return loss / len(loader.dataset) + +if __name__ == "__main__": + print("2CYCLES TRAIN") + + parser = argparse.ArgumentParser(description='2CYCLES') + parser.add_argument('--device', type= int, default= 0) + + parser.add_argument('--max_process_on_device', type=int) + parser.add_argument('--readout', type=str, default="extph") + parser.add_argument('--exp_name', type= str, default= '2cycles', help= 'experiment name to save as') + parser.add_argument('--dataset_name', type= str, default= 'Simple2Cycles') + # parser.add_argument('--selfsupervised', dest='ssl', default= True, type= bool)#action='store_false') + # parser.add_argument('--evaluation', type=str, default= 'SVC', choices= ['RandomForest', 'LogisticRegression', 'SVC'], help= 'downstream evaluation protocol classifier type') + parser.add_argument('--seed', dest= 'seed', default= 0) + parser.add_argument('--verbose', type= bool, default= True) + parser.add_argument('--sup_combo', dest= 'sup', default= True, type= bool) + parser.add_argument('--lr', type= float, default= 0.01) + parser.add_argument('--lr_drop_fact', type= float, default= 0.5) + parser.add_argument('--num_epochs', type=int, default=100) + parser.add_argument('--epoch_step', type=int, default=30) + parser.add_argument('--batch_size', type=int, default=128)#512) + parser.add_argument('--weight_decay', type=float, default=0.0) + parser.add_argument('--use_super_level_set_filtration', type=bool, default=True) + parser.add_argument('--use_raw_node_label', type= bool, default= False) + parser.add_argument('--use_node_degree', type=bool, default=True) + parser.add_argument('--set_node_degree_uninformative', type=bool, default=False) + parser.add_argument('--use_node_label', type=bool, default=False) + parser.add_argument('--filt_conv_number', type=int, default=1) + parser.add_argument('--filt_conv_dimension', type=int, default=128) + parser.add_argument('--conv_number', type=int, default=5) + parser.add_argument('--conv_dimension', type=int, default=128) + parser.add_argument('--gin_mlp_type', type=str, default='lin_bn_lrelu_lin') + parser.add_argument('--num_struct_elements', type=int, default=128) + parser.add_argument('--cls_hidden_dimension', type=int, default=512) + parser.add_argument('--drop_out', type=float, default=0.5) + parser.add_argument('--output_dir', type=str, default= 'results') + parser.set_defaults(ssl=False) + + args = parser.parse_args() + + device= args.device + dataset = dataset_factory(args.dataset_name, verbose=args.verbose) + + if args.readout == "extph": + model = PershomLearnedFiltSupCombo(dataset, args.use_super_level_set_filtration, args.use_node_degree, + args.set_node_degree_uninformative, args.use_node_label, + args.use_raw_node_label, + args.filt_conv_number, args.filt_conv_dimension, args.gin_mlp_type, + args.num_struct_elements, args.cls_hidden_dimension, args.drop_out, + conv_number=args.conv_number, conv_dimension=args.conv_dimension, aug=None, + readout=args.readout).to(device) + else: + model = ClassicReadoutFilt(dataset, args.use_super_level_set_filtration, args.use_node_degree, + args.set_node_degree_uninformative, args.use_node_label, + args.use_raw_node_label, + args.filt_conv_number, args.filt_conv_dimension, args.gin_mlp_type, + args.num_struct_elements, args.cls_hidden_dimension, args.drop_out, + conv_number=args.conv_number, conv_dimension=args.conv_dimension, aug=None, + readout=args.readout).to(device) + cross_validation_with_val_set(args, dataset, model, 10, args.num_epochs, args.batch_size, + args.lr, lr_decay_factor=0.5, lr_decay_step_size= 50, + weight_decay=0) diff --git a/train/__pycache__/imdb-multi_train_eval.cpython-38.pyc b/train/__pycache__/imdb-multi_train_eval.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a33bceea536330953b19e8ac677b8808714dfd34 GIT binary patch literal 8973 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torch.utils.tensorboard import SummaryWriter +writer= SummaryWriter() + +import numpy as np + +import torch.optim as optim + +from torch.optim.lr_scheduler import MultiStepLR + +torch.backends.cudnn.deterministic = True +# torch.manual_seed(12345) +# torch.cuda.manual_seed_all(12345) +# random.seed(12345) +# np.random.seed(12345) + +from gnn.dd_model import PershomLearnedFilt, PershomLearnedFiltSup, PershomRigidDegreeFilt, GIN, SimpleNNBaseline, ClassicGNN, ClassicReadoutFilt +from data.data import dataset_factory, train_test_val_split, Subset +from data.utils import my_collate + + +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + +class Subset(torch.utils.data.Dataset): + def __init__(self, dataset, indices): + assert isinstance(indices, (list, tuple)) + self.ds = dataset + self.indices = tuple(indices) + self.y = dataset.y[indices] + assert len(indices) <= len(dataset) + + def __len__(self): + return len(self.indices) + + def __getitem__(self, idx): + return self.ds[self.indices[idx]] + + +def cross_validation_with_val_set(args, dataset, model, folds, epochs, batch_size, + lr, lr_decay_factor, lr_decay_step_size, + weight_decay, logger=None): + + val_losses, accs, durations = [], [], [] + for fold, (train_idx, test_idx, + val_idx) in enumerate(zip(*k_fold(dataset, folds, args.device))): + # print("train_idx: ", train_idx) + tr_dataset = Subset(dataset, train_idx) + te_dataset = Subset(dataset, test_idx) + val_dataset = Subset(dataset, val_idx) + dl_train = torch.utils.data.DataLoader( + tr_dataset, + collate_fn=my_collate, + batch_size=args.batch_size, # make this BIG for better contrastive learning + shuffle=True, + # if last batch would have size 1 we have to drop it ... + drop_last=(len(tr_dataset) % args.batch_size == 1) + ) + + dl_val = torch.utils.data.DataLoader( + val_dataset, + collate_fn=my_collate, + batch_size=1, + shuffle=False, + # drop_last = (len(dataset) % 64 == 1) + ) + dl_test = torch.utils.data.DataLoader( + te_dataset, + collate_fn=my_collate, + batch_size=1, + shuffle=False, + # drop_last = (len(dataset) % 64 == 1) + ) + model.to(device).reset_parameters() + optimizer = Adam(model.parameters(), lr=lr, weight_decay=weight_decay) + + if torch.cuda.is_available(): + torch.cuda.synchronize() + + t_start = time.perf_counter() + + for epoch in range(1, epochs + 1): + train_loss = train_ph(args, model, optimizer, dl_train) + val_losses.append(eval_loss(model, dl_val)) + accs.append(eval_acc(model, dl_test)) + eval_info = { + 'fold': fold, + 'epoch': epoch, + 'train_loss': train_loss, + 'val_loss': val_losses[-1], + 'test_acc': accs[-1], + } + print(eval_info, flush= True) + + writer.add_scalars('loss_' + args.exp_name, {'train_loss_fold'+str(fold): float(train_loss)}, epoch) + writer.add_scalars('acc_' + args.exp_name, {'test_acc_fold'+str(fold): float(accs[-1])}, epoch) + if logger is not None: + logger(eval_info) + + if epoch % lr_decay_step_size == 0: + for param_group in optimizer.param_groups: + param_group['lr'] = lr_decay_factor * param_group['lr'] + + if torch.cuda.is_available(): + torch.cuda.synchronize() + + t_end = time.perf_counter() + durations.append(t_end - t_start) + + loss, acc, duration = tensor(val_losses), tensor(accs), tensor(durations) + fold_i= fold+1 + loss, acc = loss.view(fold_i, epochs), acc.view(fold_i, epochs) + loss, argmin = loss.min(dim=1) + acc = acc[torch.arange(fold_i, dtype=torch.long), argmin] + + loss_mean = loss.mean().item() + acc_mean = acc.mean().item() + acc_std = acc.std().item() + duration_mean = duration.mean().item() + print('AFTER FOLD '+str(fold)+f'Val Loss: {loss_mean:.4f}, Test Accuracy: {acc_mean:.3f} ' + f'± {acc_std:.3f}, Duration: {duration_mean:.3f}', flush= True) + loss, acc, duration = tensor(val_losses), tensor(accs), tensor(durations) + loss, acc = loss.view(folds, epochs), acc.view(folds, epochs) + loss, argmin = loss.min(dim=1) + acc = acc[torch.arange(folds, dtype=torch.long), argmin] + + loss_mean = loss.mean().item() + acc_mean = acc.mean().item() + acc_std = acc.std().item() + duration_mean = duration.mean().item() + print(f'Val Loss: {loss_mean:.4f}, Test Accuracy: {acc_mean:.3f} ' + f'± {acc_std:.3f}, Duration: {duration_mean:.3f}', flush= True) + + return loss_mean, acc_mean, acc_std + + +def k_fold(dataset, folds, device): + skf = StratifiedKFold(folds, shuffle=True, random_state=12345) + + test_indices, train_indices = [], [] + y= torch.tensor([dataset[i].y for i in range(len(dataset))], dtype= torch.long) + for _, idx in skf.split(torch.zeros(len(dataset)), y): + test_indices.append(list(idx)) + + val_indices = [test_indices[i - 1] for i in range(folds)] + + for i in range(folds): + train_mask = torch.ones(len(dataset), dtype=torch.bool) + train_mask[test_indices[i]] = 0 + train_mask[val_indices[i]] = 0 + # train_indices.append(train_mask.nonzero(as_tuple=False).view(-1)) + train_indices.append(list(train_mask.nonzero(as_tuple=False).view(-1).cpu().numpy())) + return (list(train_indices)), (list(test_indices)), (list(val_indices)) + +def num_graphs(data): + if hasattr(data, 'num_graphs'): + return data.num_graphs + else: + return data.x.size(0) + +def train_ph(args, model, opt, dl_train): + model.train() + + total_loss = 0 + + for batch_i, batch in enumerate(dl_train, start=1): + opt.zero_grad() + batch = batch.to(device) + if not hasattr(batch, 'node_lab'): batch.node_lab = None + batch.boundary_info = [e.to(device) for e in batch.boundary_info] + logit= (model(batch,device)).to(device) + loss1 = F.nll_loss(logit.float(), batch.y.view(-1).long()) + loss= loss1 + loss.backward() + total_loss += loss.item() * num_graphs(batch) + opt.step() + return total_loss / len(dl_train.dataset) + +def train(model, optimizer, loader): + model.train() + + total_loss = 0 + for data in loader: + optimizer.zero_grad() + data = data.to(device) + out = model(data, device) + loss = F.nll_loss(out.float(), data.y.view(-1).long()) + loss.backward() + total_loss += loss.item() * num_graphs(data) + optimizer.step() + return total_loss / len(loader.dataset) + + +def eval_acc(model, loader): + model.eval() + + correct = 0 + for data in loader: + data = data.to(device) + with torch.no_grad(): + pred = model(data, device).max(1)[1] + correct += pred.eq(data.y.view(-1)).sum().item() + return correct / len(loader.dataset) + + +def eval_loss(model, loader): + model.eval() + + loss = 0 + for data in loader: + data = data.to(device) + with torch.no_grad(): + out = model(data, device) + loss += F.nll_loss(out.float(), data.y.view(-1).long(), reduction='sum').item() + return loss / len(loader.dataset) + +if __name__ == "__main__": + print("DD TRAIN") + + parser = argparse.ArgumentParser(description='DD') + + parser.add_argument('--device', type= int, default= 0) + + parser.add_argument('--max_process_on_device', type=int) + parser.add_argument('--readout', type=str, default="extph") + parser.add_argument('--exp_name', type= str, default= 'dd', help= 'experiment name to save as') + parser.add_argument('--dataset_name', type= str, default= 'DD') + # parser.add_argument('--selfsupervised', dest='ssl', default= True, type= bool)#action='store_false') + # parser.add_argument('--evaluation', type=str, default= 'SVC', choices= ['RandomForest', 'LogisticRegression', 'SVC'], help= 'downstream evaluation protocol classifier type') + parser.add_argument('--seed', dest= 'seed', default= 0) + parser.add_argument('--verbose', type= bool, default= True) + parser.add_argument('--sup_combo', dest= 'sup', default= True, type= bool) + parser.add_argument('--lr', type= float, default= 0.01) + parser.add_argument('--lr_drop_fact', type= float, default= 0.5) + parser.add_argument('--num_epochs', type=int, default=100) + parser.add_argument('--epoch_step', type=int, default=30) + parser.add_argument('--batch_size', type=int, default=128)#512) + parser.add_argument('--weight_decay', type=float, default=0.0) + parser.add_argument('--use_super_level_set_filtration', type=bool, default=True) + parser.add_argument('--use_raw_node_label', type= bool, default= True) + parser.add_argument('--use_node_degree', type=bool, default=True) + parser.add_argument('--set_node_degree_uninformative', type=bool, default=False) + parser.add_argument('--use_node_label', type=bool, default=True) + parser.add_argument('--filt_conv_number', type=int, default=1) + parser.add_argument('--filt_conv_dimension', type=int, default=128) + parser.add_argument('--conv_number', type=int, default=5) + parser.add_argument('--conv_dimension', type=int, default=128) + parser.add_argument('--gin_mlp_type', type=str, default='lin_bn_lrelu_lin') + parser.add_argument('--num_struct_elements', type=int, default=128) + parser.add_argument('--cls_hidden_dimension', type=int, default=512) + parser.add_argument('--drop_out', type=float, default=0.5) + parser.add_argument('--output_dir', type=str, default= 'results') + parser.set_defaults(ssl=False) + + args = parser.parse_args() + print(args) + + device= args.device + + dataset = dataset_factory(args.dataset_name, verbose=args.verbose) + + if args.readout == "extph": + model = PershomLearnedFiltSup(dataset, args.use_super_level_set_filtration, args.use_node_degree, + args.set_node_degree_uninformative, args.use_node_label, + args.use_raw_node_label, + args.filt_conv_number, args.filt_conv_dimension, args.gin_mlp_type, + args.num_struct_elements, args.cls_hidden_dimension, args.drop_out, + conv_number=args.conv_number, conv_dimension=args.conv_dimension, aug=None, + readout=args.readout).to(device) + else: + model = ClassicReadoutFilt(dataset, args.use_super_level_set_filtration, args.use_node_degree, + args.set_node_degree_uninformative, args.use_node_label, + args.use_raw_node_label, + args.filt_conv_number, args.filt_conv_dimension, args.gin_mlp_type, + args.num_struct_elements, args.cls_hidden_dimension, args.drop_out, + conv_number=args.conv_number, conv_dimension=args.conv_dimension, aug=None, + readout=args.readout).to(device) + + cross_validation_with_val_set(args, dataset, model, 10, args.num_epochs, args.batch_size, + args.lr, lr_decay_factor=0.5, lr_decay_step_size= 50, + weight_decay=0) diff --git a/train/imdb-multi_train_eval.py b/train/imdb-multi_train_eval.py new file mode 100644 index 0000000..b190778 --- /dev/null +++ b/train/imdb-multi_train_eval.py @@ -0,0 +1,292 @@ +import time +import sys + +import torch +import torch.nn.functional as F +from torch import tensor +from torch.optim import Adam +from sklearn.model_selection import StratifiedKFold +#from torch_geometric.loader import DataLoader, DenseDataLoader as DenseLoader +from torch.utils.data import DataLoader +from torch.utils.tensorboard import SummaryWriter +writer= SummaryWriter() + +import sys +sys.path.append(".") + +import os.path as osp +import uuid +import pickle +import datetime +import random +import torch +import os + +import argparse + +from torch.utils.tensorboard import SummaryWriter +writer= SummaryWriter() + +import numpy as np + +import torch.optim as optim + +from torch.optim.lr_scheduler import MultiStepLR + +torch.backends.cudnn.deterministic = True +# torch.manual_seed(12345) +# torch.cuda.manual_seed_all(12345) +# random.seed(12345) +# np.random.seed(12345) + +from gnn.imdbmulti_model import PershomLearnedFilt, PershomLearnedFiltSup, PershomRigidDegreeFilt, GIN, SimpleNNBaseline, ClassicGNN, ClassicReadoutFilt +from data.data import dataset_factory, train_test_val_split, Subset +from data.utils import my_collate + + +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + +class Subset(torch.utils.data.Dataset): + def __init__(self, dataset, indices): + assert isinstance(indices, (list, tuple)) + self.ds = dataset + self.indices = tuple(indices) + self.y = dataset.y[indices] + assert len(indices) <= len(dataset) + + def __len__(self): + return len(self.indices) + + def __getitem__(self, idx): + return self.ds[self.indices[idx]] + + +def cross_validation_with_val_set(args, dataset, model, folds, epochs, batch_size, + lr, lr_decay_factor, lr_decay_step_size, + weight_decay, logger=None): + + val_losses, accs, durations = [], [], [] + for fold, (train_idx, test_idx, + val_idx) in enumerate(zip(*k_fold(dataset, folds, args.device))): + + tr_dataset = Subset(dataset, train_idx) + te_dataset = Subset(dataset, test_idx) + val_dataset = Subset(dataset, val_idx) + + dl_train = torch.utils.data.DataLoader( + tr_dataset, + collate_fn=my_collate, + batch_size=args.batch_size, # make this BIG for better contrastive learning + shuffle=True, + # if last batch would have size 1 we have to drop it ... + drop_last=(len(tr_dataset) % args.batch_size == 1) + ) + + dl_val = torch.utils.data.DataLoader( + val_dataset, + collate_fn=my_collate, + batch_size=1, + shuffle=False, + # drop_last = (len(dataset) % 64 == 1) + ) + dl_test = torch.utils.data.DataLoader( + te_dataset, + collate_fn=my_collate, + batch_size=1, + shuffle=False, + # drop_last = (len(dataset) % 64 == 1) + ) + model.to(device).reset_parameters() + optimizer = Adam(model.parameters(), lr=lr, weight_decay=weight_decay) + + if torch.cuda.is_available(): + torch.cuda.synchronize() + + t_start = time.perf_counter() + + for epoch in range(1, epochs + 1): + train_loss = train_ph(args, model, optimizer, dl_train) + val_losses.append(eval_loss(model, dl_val)) + accs.append(eval_acc(model, dl_test)) + eval_info = { + 'fold': fold, + 'epoch': epoch, + 'train_loss': train_loss, + 'val_loss': val_losses[-1], + 'test_acc': accs[-1], + } + print(eval_info) + + writer.add_scalars('loss_' + args.exp_name, {'train_loss_fold'+str(fold): float(train_loss)}, epoch) + writer.add_scalars('acc_' + args.exp_name, {'test_acc_fold'+str(fold): float(accs[-1])}, epoch) + if logger is not None: + logger(eval_info) + + if epoch % lr_decay_step_size == 0: + for param_group in optimizer.param_groups: + param_group['lr'] = lr_decay_factor * param_group['lr'] + + if torch.cuda.is_available(): + torch.cuda.synchronize() + + t_end = time.perf_counter() + durations.append(t_end - t_start) + + loss, acc, duration = tensor(val_losses), tensor(accs), tensor(durations) + loss, acc = loss.view(folds, epochs), acc.view(folds, epochs) + loss, argmin = loss.min(dim=1) + acc = acc[torch.arange(folds, dtype=torch.long), argmin] + + loss_mean = loss.mean().item() + acc_mean = acc.mean().item() + acc_std = acc.std().item() + duration_mean = duration.mean().item() + print(f'Val Loss: {loss_mean:.4f}, Test Accuracy: {acc_mean:.3f} ' + f'± {acc_std:.3f}, Duration: {duration_mean:.3f}') + + return loss_mean, acc_mean, acc_std + + +def k_fold(dataset, folds, device): + skf = StratifiedKFold(folds, shuffle=True, random_state=12345) + + test_indices, train_indices = [], [] + y= torch.tensor([dataset[i].y for i in range(len(dataset))], dtype= torch.long) + + for _, idx in skf.split(torch.zeros(len(dataset)), y): + test_indices.append(list(idx)) + + val_indices = [test_indices[i - 1] for i in range(folds)] + + for i in range(folds): + train_mask = torch.ones(len(dataset), dtype=torch.bool) + train_mask[test_indices[i]] = 0 + train_mask[val_indices[i]] = 0 + # train_indices.append(train_mask.nonzero(as_tuple=False).view(-1)) + train_indices.append(list(train_mask.nonzero(as_tuple=False).view(-1).cpu().numpy())) + return (list(train_indices)), (list(test_indices)), (list(val_indices)) + +def num_graphs(data): + if hasattr(data, 'num_graphs'): + return data.num_graphs + else: + return data.x.size(0) + +def train_ph(args, model, opt, dl_train): + model.train() + + total_loss = 0 + + for batch_i, batch in enumerate(dl_train, start=1): + opt.zero_grad() + batch = batch.to(device) + if not hasattr(batch, 'node_lab'): batch.node_lab = None + batch.boundary_info = [e.to(device) for e in batch.boundary_info] + logit= (model(batch,device)).to(device) + loss1 = F.nll_loss(logit.float(), batch.y.view(-1).long()) + loss= loss1 + loss.backward() + total_loss += loss.item() * num_graphs(batch) + opt.step() + return total_loss / len(dl_train.dataset) + +def train(model, optimizer, loader): + model.train() + + total_loss = 0 + for data in loader: + optimizer.zero_grad() + data = data.to(device) + out = model(data, device) + loss = F.nll_loss(out.float(), data.y.view(-1).long()) + loss.backward() + total_loss += loss.item() * num_graphs(data) + optimizer.step() + return total_loss / len(loader.dataset) + + +def eval_acc(model, loader): + model.eval() + + correct = 0 + for data in loader: + data = data.to(device) + with torch.no_grad(): + pred = model(data, device).max(1)[1] + correct += pred.eq(data.y.view(-1)).sum().item() + return correct / len(loader.dataset) + + +def eval_loss(model, loader): + model.eval() + + loss = 0 + for data in loader: + data = data.to(device) + with torch.no_grad(): + out = model(data, device) + loss += F.nll_loss(out.float(), data.y.view(-1).long(), reduction='sum').item() + return loss / len(loader.dataset) + +def arg_bool(s): + return s != 'False' + +if __name__ == "__main__": + print("IMDB-MULTI TRAIN") + parser = argparse.ArgumentParser(description='IMDB-MULTI') + parser.add_argument('--device', type= int, default= 0) + + parser.add_argument('--max_process_on_device', type=int) + + parser.add_argument('--exp_name', type= str, default= 'imdb-multi', help= 'experiment name to save as') + parser.add_argument('--dataset_name', type= str, default= 'IMDB-MULTI') + parser.add_argument('--readout', type= str, default= "extph") + parser.add_argument('--seed', dest= 'seed', default= 0) + parser.add_argument('--verbose', type= bool, default= True) + parser.add_argument('--sup_combo', dest= 'sup', default= True, type= bool) + parser.add_argument('--lr', type= float, default= 0.01) + parser.add_argument('--lr_drop_fact', type= float, default= 0.5) + parser.add_argument('--num_epochs', type=int, default=100) + parser.add_argument('--epoch_step', type=int, default=30) + parser.add_argument('--batch_size', type=int, default=128)#512) + parser.add_argument('--weight_decay', type=float, default=0.0) + parser.add_argument('--use_super_level_set_filtration', type=arg_bool, default=True) + parser.add_argument('--use_raw_node_label', type= arg_bool, default= False) + parser.add_argument('--use_node_degree', type=arg_bool, default=True) + parser.add_argument('--set_node_degree_uninformative', type=arg_bool, default=False) + parser.add_argument('--use_node_label', type=arg_bool, default=False) + parser.add_argument('--filt_conv_number', type=int, default=1) + parser.add_argument('--filt_conv_dimension', type=int, default=128) + parser.add_argument('--conv_number', type=int, default=5) + parser.add_argument('--conv_dimension', type=int, default=128) + parser.add_argument('--gin_mlp_type', type=str, default='lin_bn_lrelu_lin') + parser.add_argument('--num_struct_elements', type=int, default=128) + parser.add_argument('--cls_hidden_dimension', type=int, default=512) + parser.add_argument('--drop_out', type=float, default=0.5) + parser.add_argument('--output_dir', type=str, default= 'results') + parser.set_defaults(ssl=False) + + args = parser.parse_args() + print(args) + + device= args.device + dataset = dataset_factory(args.dataset_name, verbose=args.verbose) + if args.readout=="extph": + model = PershomLearnedFiltSup(dataset, args.use_super_level_set_filtration, args.use_node_degree, + args.set_node_degree_uninformative, args.use_node_label, args.use_raw_node_label, + args.filt_conv_number, args.filt_conv_dimension, args.gin_mlp_type, + args.num_struct_elements, args.cls_hidden_dimension, args.drop_out, + conv_number=args.conv_number, conv_dimension=args.conv_dimension, aug=None, readout= args.readout).to(device) + else: + model = ClassicReadoutFilt(dataset, args.use_super_level_set_filtration, args.use_node_degree, + args.set_node_degree_uninformative, args.use_node_label, + args.use_raw_node_label, + args.filt_conv_number, args.filt_conv_dimension, args.gin_mlp_type, + args.num_struct_elements, args.cls_hidden_dimension, args.drop_out, + conv_number=args.conv_number, conv_dimension=args.conv_dimension, aug=None, + readout=args.readout).to(device) + + cross_validation_with_val_set(args, dataset, model, 10, args.num_epochs, args.batch_size, + args.lr, lr_decay_factor=0.5, lr_decay_step_size= 50, + weight_decay=0) diff --git a/train/mol_train_eval.py b/train/mol_train_eval.py new file mode 100644 index 0000000..108e51d --- /dev/null +++ b/train/mol_train_eval.py @@ -0,0 +1,371 @@ +import time +import sys + +import torch +import torch.nn.functional as F +from torch import tensor +from torch.optim import Adam +from sklearn.model_selection import StratifiedKFold +# from torch_geometric.loader import DataLoader, DenseDataLoader as DenseLoader +from torch.utils.data import DataLoader +from torch.utils.tensorboard import SummaryWriter +writer= SummaryWriter() + +import sys +sys.path.append(".") + +import os.path as osp +import uuid +import pickle +import datetime +import random +import torch +import os + +import argparse + +from torch.utils.tensorboard import SummaryWriter +writer= SummaryWriter() + +import numpy as np + +import torch.optim as optim + +from torch.optim.lr_scheduler import MultiStepLR + +torch.backends.cudnn.deterministic = True +# torch.manual_seed(12345) +# torch.cuda.manual_seed_all(12345) +# random.seed(12345) +# np.random.seed(12345) + +from gnn.dd_model import PershomLearnedFilt, PershomLearnedFiltSup, PershomRigidDegreeFilt, GIN, SimpleNNBaseline, ClassicGNN, ClassicReadoutFilt +from data.data import dataset_factory, train_test_val_split, Subset +from data.utils import my_collate + +from ogb.graphproppred import Evaluator + +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + +class Subset(torch.utils.data.Dataset): + def __init__(self, dataset, indices): + assert isinstance(indices, (list, tuple)) + self.ds = dataset + self.indices = tuple(indices) + self.y = dataset.y[indices] + assert len(indices) <= len(dataset) + + def __len__(self): + return len(self.indices) + + def __getitem__(self, idx): + return self.ds[self.indices[idx]] + + +def cross_validation_with_val_set(args, dataset, model, folds, epochs, batch_size, + lr, lr_decay_factor, lr_decay_step_size, + weight_decay, logger=None): + + val_losses, accs, durations = [], [], [] + for fold, (train_idx, test_idx, + val_idx) in enumerate(zip(*k_fold(dataset, folds, args.device))): + # print("train_idx: ", train_idx) + tr_dataset = Subset(dataset, train_idx) + te_dataset = Subset(dataset, test_idx) + val_dataset = Subset(dataset, val_idx) + dl_train = torch.utils.data.DataLoader( + tr_dataset, + collate_fn=my_collate, + batch_size=args.batch_size, # make this BIG for better contrastive learning + shuffle=True, + # if last batch would have size 1 we have to drop it ... + drop_last=(len(tr_dataset) % args.batch_size == 1) + ) + + dl_val = torch.utils.data.DataLoader( + val_dataset, + collate_fn=my_collate, + batch_size=1, + shuffle=False, + # drop_last = (len(dataset) % 64 == 1) + ) + dl_test = torch.utils.data.DataLoader( + te_dataset, + collate_fn=my_collate, + batch_size=1, + shuffle=False, + # drop_last = (len(dataset) % 64 == 1) + ) + model.to(device).reset_parameters() + optimizer = Adam(model.parameters(), lr=lr, weight_decay=weight_decay) + + if torch.cuda.is_available(): + torch.cuda.synchronize() + + t_start = time.perf_counter() + + for epoch in range(1, epochs + 1): + train_loss = train_ph(args, model, optimizer, dl_train) + val_losses.append(eval_loss(model, dl_val)) + accs.append(eval_rocauc(model, dl_test, args.dataset_name)) + eval_info = { + 'fold': fold, + 'epoch': epoch, + 'train_loss': train_loss, + 'val_loss': val_losses[-1], + 'test_score': accs[-1], + } + print(eval_info, flush= True) + + writer.add_scalars('loss_' + args.exp_name, {'train_loss_fold'+str(fold): float(train_loss)}, epoch) + writer.add_scalars('score_' + args.exp_name, {'test_score_fold'+str(fold): float(accs[-1])}, epoch) + if logger is not None: + logger(eval_info) + + if epoch % lr_decay_step_size == 0: + for param_group in optimizer.param_groups: + param_group['lr'] = lr_decay_factor * param_group['lr'] + + if torch.cuda.is_available(): + torch.cuda.synchronize() + + t_end = time.perf_counter() + durations.append(t_end - t_start) + + loss, acc, duration = tensor(val_losses), tensor(accs), tensor(durations) + fold_i= fold+1 + loss, acc = loss.view(fold_i, epochs), acc.view(fold_i, epochs) + loss, argmin = loss.min(dim=1) + acc = acc[torch.arange(fold_i, dtype=torch.long), argmin] + + loss_mean = loss.mean().item() + acc_mean = acc.mean().item() + acc_std = acc.std().item() + duration_mean = duration.mean().item() + print('AFTER FOLD '+str(fold)+f'Val Loss: {loss_mean:.4f}, Test Score: {acc_mean:.3f} ' + f'± {acc_std:.3f}, Duration: {duration_mean:.3f}', flush= True) + loss, acc, duration = tensor(val_losses), tensor(accs), tensor(durations) + loss, acc = loss.view(folds, epochs), acc.view(folds, epochs) + loss, argmin = loss.min(dim=1) + acc = acc[torch.arange(folds, dtype=torch.long), argmin] + + loss_mean = loss.mean().item() + acc_mean = acc.mean().item() + acc_std = acc.std().item() + duration_mean = duration.mean().item() + print(f'Val Loss: {loss_mean:.4f}, Test Score: {acc_mean:.3f} ' + f'± {acc_std:.3f}, Duration: {duration_mean:.3f}', flush= True) + + return loss_mean, acc_mean, acc_std + + +def k_fold(dataset, folds, device): + skf = StratifiedKFold(folds, shuffle=True, random_state=12345) + + test_indices, train_indices = [], [] + y= torch.tensor([dataset[i].y for i in range(len(dataset))], dtype= torch.long) + for _, idx in skf.split(torch.zeros(len(dataset)), y): + test_indices.append(list(idx)) + + val_indices = [test_indices[i - 1] for i in range(folds)] + + for i in range(folds): + train_mask = torch.ones(len(dataset), dtype=torch.bool) + train_mask[test_indices[i]] = 0 + train_mask[val_indices[i]] = 0 + # train_indices.append(train_mask.nonzero(as_tuple=False).view(-1)) + train_indices.append(list(train_mask.nonzero(as_tuple=False).view(-1).cpu().numpy())) + return (list(train_indices)), (list(test_indices)), (list(val_indices)) + +def num_graphs(data): + if hasattr(data, 'num_graphs'): + return data.num_graphs + else: + return data.x.size(0) + +def train_ph(args, model, opt, dl_train): + model.train() + + total_loss = 0 + + for batch_i, batch in enumerate(dl_train, start=1): + opt.zero_grad() + batch = batch.to(device) + if not hasattr(batch, 'node_lab'): batch.node_lab = None + batch.boundary_info = [e.to(device) for e in batch.boundary_info] + logit= (model(batch,device)).to(device) + loss1 = F.nll_loss(logit.float(), batch.y.view(-1).long()) + loss= loss1 + loss.backward() + total_loss += loss.item() * num_graphs(batch) + opt.step() + return total_loss / len(dl_train.dataset) + +def train(model, optimizer, loader): + model.train() + + total_loss = 0 + for data in loader: + optimizer.zero_grad() + data = data.to(device) + out = model(data, device) + loss = F.nll_loss(out.float(), data.y.view(-1).long()) + loss.backward() + total_loss += loss.item() * num_graphs(data) + optimizer.step() + return total_loss / len(loader.dataset) + +def eval_rocauc(model, loader, dataset_name): + #dataset_name= 'ogbg-molhiv' + evaluator = Evaluator(name=dataset_name) + # print(evaluator.expected_input_format) + # print(evaluator.expected_output_format) + model.eval() + prediction= [] + y_test= [] + for data in loader: + data = data.to(device) + with torch.no_grad(): + pred = model(data, device).max(1)[1] + prediction+= list(pred.view(-1).cpu().numpy()) + y_test+= list(data.y.view(-1).cpu().numpy()) + result_dict = evaluator.eval({'y_pred': np.asarray(prediction).reshape(len(prediction), 1), + 'y_true': np.asarray(y_test).reshape(len(y_test), 1)}) + score= 0.0 + if dataset_name == 'ogbg-ppa': + # print("ACC computed") + # print("DATASET: ", dataset_name) + # print(result_dict) + # print("ACC as computed by standard metric module: ", acc) + # f = open(output_dir + "/_RES_" + exp_name + ".res", "a") + # f.write("DATASET: " + dataset_name + ", SSL: " + str(ssl) + ", time: " + str( + # datetime.datetime.now()) + ", experiment: " + exp_name + "\n") + # f.writemollip("ACC after grid search : " + str(result_dict['acc']) + "\n") + # f.close() + score= (result_dict['acc']) + elif dataset_name in ['ogbg-molhiv', 'ogbg-molbace', + 'ogbg-molbbbp', + 'ogbg-molclintox', + 'ogbg-molsider', + 'ogbg-moltox21', + 'ogbg-moltoxcast']: + #print("ROCAUC computed") + #print("DATASET: ", dataset_name) + #print(result_dict) + #f = open(output_dir + "/_RES_" + exp_name + ".res", "a") + #f.write("DATASET: " + dataset_name + ", SSL: " + str(ssl) + ", time: " + str( + # datetime.datetime.now()) + ", experiment: " + exp_name + "\n") + #f.write("ROAUC after grid search : " + str(result_dict['rocauc']) + "\n") + #f.close() + score= (result_dict['rocauc']) + elif dataset_name == 'ogbg-code2': + # print("F1 computed") + # print("DATASET: ", dataset_name) + # print(result_dict) + # f = open(output_dir + "/_RES_" + exp_name + ".res", "a") + # f.write("DATASET: " + dataset_name + ", SSL: " + str(ssl) + ", time: " + str( + # datetime.datetime.now()) + ", experiment: " + exp_name + "\n") + # f.write("F1 after grid search : " + str(result_dict['rocauc']) + "\n") + # f.close() + score =(result_dict['f1']) + elif dataset_name in ['ogbg-molmuv', 'ogbg-molpcba']: + # print("AP computed") + # print("DATASET: ", dataset_name) + # print(result_dict) + # f = open(output_dir + "/_RES_" + exp_name + ".res", "a") + # f.write("DATASET: " + dataset_name + ", SSL: " + str(ssl) + ", time: " + str( + # datetime.datetime.now()) + ", experiment: " + exp_name + "\n") + # f.write("AP after grid search : " + str(result_dict['rocauc']) + "\n") + # f.close() + score= (result_dict['ap']) + return score + +def eval_acc(model, loader): + model.eval() + + correct = 0 + for data in loader: + data = data.to(device) + with torch.no_grad(): + pred = model(data, device).max(1)[1] + correct += pred.eq(data.y.view(-1)).sum().item() + return correct / len(loader.dataset) + + +def eval_loss(model, loader): + model.eval() + + loss = 0 + for data in loader: + data = data.to(device) + with torch.no_grad(): + out = model(data, device) + loss += F.nll_loss(out.float(), data.y.view(-1).long(), reduction='sum').item() + return loss / len(loader.dataset) + +if __name__ == "__main__": + print("MOL TRAIN") + + parser = argparse.ArgumentParser(description='MOL') + + parser.add_argument('--device', type= int, default= 0) + + parser.add_argument('--max_process_on_device', type=int) + parser.add_argument('--readout', type=str, default="extph") + parser.add_argument('--exp_name', type= str, default= 'ogbg-molbbbp', help= 'experiment name to save as') + parser.add_argument('--dataset_name', type= str, default= 'ogbg-molbbbp') + # parser.add_argument('--selfsupervised', dest='ssl', default= True, type= bool)#action='store_false') + # parser.add_argument('--evaluation', type=str, default= 'SVC', choices= ['RandomForest', 'LogisticRegression', 'SVC'], help= 'downstream evaluation protocol classifier type') + parser.add_argument('--seed', dest= 'seed', default= 0) + parser.add_argument('--verbose', type= bool, default= True) + parser.add_argument('--sup_combo', dest= 'sup', default= True, type= bool) + parser.add_argument('--lr', type= float, default= 0.01) + parser.add_argument('--lr_drop_fact', type= float, default= 0.5) + parser.add_argument('--num_epochs', type=int, default=100) + parser.add_argument('--epoch_step', type=int, default=30) + parser.add_argument('--batch_size', type=int, default=128)#512) + parser.add_argument('--weight_decay', type=float, default=0.0) + parser.add_argument('--use_super_level_set_filtration', type=bool, default=True) + parser.add_argument('--use_raw_node_label', type= bool, default= True) + parser.add_argument('--use_node_degree', type=bool, default=True) + parser.add_argument('--set_node_degree_uninformative', type=bool, default=False) + parser.add_argument('--use_node_label', type=bool, default=True) + parser.add_argument('--filt_conv_number', type=int, default=1) + parser.add_argument('--filt_conv_dimension', type=int, default=128) + parser.add_argument('--conv_number', type=int, default=5) + parser.add_argument('--conv_dimension', type=int, default=128) + parser.add_argument('--gin_mlp_type', type=str, default='lin_bn_lrelu_lin') + parser.add_argument('--num_struct_elements', type=int, default=128) + parser.add_argument('--cls_hidden_dimension', type=int, default=512) + parser.add_argument('--drop_out', type=float, default=0.5) + parser.add_argument('--output_dir', type=str, default= 'results') + parser.set_defaults(ssl=False) + + args = parser.parse_args() + print(args) + + device= args.device + + dataset = dataset_factory(args.dataset_name, verbose=args.verbose) + + if args.readout == "extph": + model = PershomLearnedFiltSup(dataset, args.use_super_level_set_filtration, args.use_node_degree, + args.set_node_degree_uninformative, args.use_node_label, + args.use_raw_node_label, + args.filt_conv_number, args.filt_conv_dimension, args.gin_mlp_type, + args.num_struct_elements, args.cls_hidden_dimension, args.drop_out, + conv_number=args.conv_number, conv_dimension=args.conv_dimension, aug=None, + readout=args.readout).to(device) + else: + model = ClassicReadoutFilt(dataset, args.use_super_level_set_filtration, args.use_node_degree, + args.set_node_degree_uninformative, args.use_node_label, + args.use_raw_node_label, + args.filt_conv_number, args.filt_conv_dimension, args.gin_mlp_type, + args.num_struct_elements, args.cls_hidden_dimension, args.drop_out, + conv_number=args.conv_number, conv_dimension=args.conv_dimension, aug=None, + readout=args.readout).to(device) + + cross_validation_with_val_set(args, dataset, model, 10, args.num_epochs, args.batch_size, + args.lr, lr_decay_factor=0.5, lr_decay_step_size= 50, + weight_decay=0) diff --git a/train/mutag_train_eval.py b/train/mutag_train_eval.py new file mode 100644 index 0000000..f55a296 --- /dev/null +++ b/train/mutag_train_eval.py @@ -0,0 +1,297 @@ +import time +import sys + +import torch +import torch.nn.functional as F +from torch import tensor +from torch.optim import Adam +from sklearn.model_selection import StratifiedKFold +# from torch_geometric.loader import DataLoader, DenseDataLoader as DenseLoader +from torch.utils.data import DataLoader +from torch.utils.tensorboard import SummaryWriter + +writer = SummaryWriter() + +import sys + +sys.path.append(".") + +import os.path as osp +import uuid +import pickle +import datetime +import random +import torch +import os + +import argparse + +from torch.utils.tensorboard import SummaryWriter + +writer = SummaryWriter() + +import numpy as np + +import torch.optim as optim + +from torch.optim.lr_scheduler import MultiStepLR + +torch.backends.cudnn.deterministic = True +# torch.manual_seed(12345) +# torch.cuda.manual_seed_all(12345) +# random.seed(12345) +# np.random.seed(12345) + +from gnn.mutag_model import PershomLearnedFilt, PershomLearnedFiltSup, PershomRigidDegreeFilt, GIN, \ + SimpleNNBaseline, ClassicGNN, ClassicReadoutFilt +from data.data import dataset_factory, train_test_val_split, Subset +from data.utils import my_collate + +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + +class Subset(torch.utils.data.Dataset): + def __init__(self, dataset, indices): + assert isinstance(indices, (list, tuple)) + self.ds = dataset + self.indices = tuple(indices) + self.y = dataset.y[indices] + assert len(indices) <= len(dataset) + + def __len__(self): + return len(self.indices) + + def __getitem__(self, idx): + return self.ds[self.indices[idx]] + + +def cross_validation_with_val_set(args, dataset, model, folds, epochs, batch_size, + lr, lr_decay_factor, lr_decay_step_size, + weight_decay, logger=None): + val_losses, accs, durations = [], [], [] + for fold, (train_idx, test_idx, + val_idx) in enumerate(zip(*k_fold(dataset, folds, args.device))): + + tr_dataset = Subset(dataset, train_idx) + te_dataset = Subset(dataset, test_idx) + val_dataset = Subset(dataset, val_idx) + + dl_train = torch.utils.data.DataLoader( + tr_dataset, + collate_fn=my_collate, + batch_size=args.batch_size, # make this BIG for better contrastive learning + shuffle=True, + # if last batch would have size 1 we have to drop it ... + drop_last=(len(tr_dataset) % args.batch_size == 1) + ) + + dl_val = torch.utils.data.DataLoader( + val_dataset, + collate_fn=my_collate, + batch_size=1, + shuffle=False, + # drop_last = (len(dataset) % 64 == 1) + ) + dl_test = torch.utils.data.DataLoader( + te_dataset, + collate_fn=my_collate, + batch_size=1, + shuffle=False, + # drop_last = (len(dataset) % 64 == 1) + ) + model.to(device).reset_parameters() + optimizer = Adam(model.parameters(), lr=lr, weight_decay=weight_decay) + + if torch.cuda.is_available(): + torch.cuda.synchronize() + + t_start = time.perf_counter() + + for epoch in range(1, epochs + 1): + train_loss = train_ph(args, model, optimizer, dl_train) + val_losses.append(eval_loss(model, dl_val)) + accs.append(eval_acc(model, dl_test)) + eval_info = { + 'fold': fold, + 'epoch': epoch, + 'train_loss': train_loss, + 'val_loss': val_losses[-1], + 'test_acc': accs[-1], + } + print(eval_info) + + writer.add_scalars('loss_' + args.exp_name, {'train_loss_fold' + str(fold): float(train_loss)}, epoch) + writer.add_scalars('acc_' + args.exp_name, {'test_acc_fold' + str(fold): float(accs[-1])}, epoch) + if logger is not None: + logger(eval_info) + + if epoch % lr_decay_step_size == 0: + for param_group in optimizer.param_groups: + param_group['lr'] = lr_decay_factor * param_group['lr'] + + if torch.cuda.is_available(): + torch.cuda.synchronize() + + t_end = time.perf_counter() + durations.append(t_end - t_start) + + loss, acc, duration = tensor(val_losses), tensor(accs), tensor(durations) + loss, acc = loss.view(folds, epochs), acc.view(folds, epochs) + loss, argmin = loss.min(dim=1) + acc = acc[torch.arange(folds, dtype=torch.long), argmin] + + loss_mean = loss.mean().item() + acc_mean = acc.mean().item() + acc_std = acc.std().item() + duration_mean = duration.mean().item() + print(f'Val Loss: {loss_mean:.4f}, Test Accuracy: {acc_mean:.3f} ' + f'± {acc_std:.3f}, Duration: {duration_mean:.3f}') + + return loss_mean, acc_mean, acc_std + + +def k_fold(dataset, folds, device): + skf = StratifiedKFold(folds, shuffle=True, random_state=12345) + + test_indices, train_indices = [], [] + y = torch.tensor([dataset[i].y for i in range(len(dataset))], dtype=torch.long) + + for _, idx in skf.split(torch.zeros(len(dataset)), y): + test_indices.append(list(idx)) + + val_indices = [test_indices[i - 1] for i in range(folds)] + + for i in range(folds): + train_mask = torch.ones(len(dataset), dtype=torch.bool) + train_mask[test_indices[i]] = 0 + train_mask[val_indices[i]] = 0 + train_indices.append(list(train_mask.nonzero(as_tuple=False).view(-1).cpu().numpy())) + return (list(train_indices)), (list(test_indices)), (list(val_indices)) + + +def num_graphs(data): + if hasattr(data, 'num_graphs'): + return data.num_graphs + else: + return data.x.size(0) + +def train_ph(args, model, opt, dl_train): + model.train() + + total_loss = 0 + + for batch_i, batch in enumerate(dl_train, start=1): + opt.zero_grad() + batch = batch.to(device) + if not hasattr(batch, 'node_lab'): batch.node_lab = None + batch.boundary_info = [e.to(device) for e in batch.boundary_info] + logit = (model(batch, device)).to(device) + loss1 = F.nll_loss(logit.float(), batch.y.view(-1).long()) + loss = loss1 + loss.backward() + total_loss += loss.item() * num_graphs(batch) + opt.step() + return total_loss / len(dl_train.dataset) + + +def train(model, optimizer, loader): + model.train() + + total_loss = 0 + for data in loader: + optimizer.zero_grad() + data = data.to(device) + out = model(data, device) + loss = F.nll_loss(out.float(), data.y.view(-1).long()) + loss.backward() + total_loss += loss.item() * num_graphs(data) + optimizer.step() + return total_loss / len(loader.dataset) + + +def eval_acc(model, loader): + model.eval() + + correct = 0 + for data in loader: + data = data.to(device) + with torch.no_grad(): + pred = model(data, device).max(1)[1] + correct += pred.eq(data.y.view(-1)).sum().item() + return correct / len(loader.dataset) + + +def eval_loss(model, loader): + model.eval() + + loss = 0 + for data in loader: + data = data.to(device) + with torch.no_grad(): + out = model(data, device) + loss += F.nll_loss(out.float(), data.y.view(-1).long(), reduction='sum').item() + return loss / len(loader.dataset) + +def arg_bool(s): + return s != 'False' + +if __name__ == "__main__": + print("MUTAG TRAIN") + parser = argparse.ArgumentParser(description='MUTAG') + parser.add_argument('--device', type=int, default=0) + + parser.add_argument('--max_process_on_device', type=int) + + parser.add_argument('--exp_name', type=str, default='mutag', help='experiment name to save as') + parser.add_argument('--dataset_name', type=str, default='MUTAG') + parser.add_argument('--readout', type=str, default="extph") + parser.add_argument('--seed', dest='seed', default=0) + parser.add_argument('--verbose', type=bool, default=True) + parser.add_argument('--sup_combo', dest='sup', default=True, type=bool) + parser.add_argument('--lr', type=float, default=0.01) + parser.add_argument('--lr_drop_fact', type=float, default=0.5) + parser.add_argument('--num_epochs', type=int, default=100) + parser.add_argument('--epoch_step', type=int, default=30) + parser.add_argument('--batch_size', type=int, default=128) # 512) + parser.add_argument('--weight_decay', type=float, default=0.0) + parser.add_argument('--use_super_level_set_filtration', type=arg_bool, default=True) + parser.add_argument('--use_raw_node_label', type=arg_bool, default=True) + parser.add_argument('--use_node_degree', type=arg_bool, default=True) + parser.add_argument('--set_node_degree_uninformative', type=arg_bool, default=False) + parser.add_argument('--use_node_label', type=arg_bool, default=False) + parser.add_argument('--filt_conv_number', type=int, default=1) + parser.add_argument('--filt_conv_dimension', type=int, default=128) + parser.add_argument('--conv_number', type=int, default=5) + parser.add_argument('--conv_dimension', type=int, default=128) + parser.add_argument('--gin_mlp_type', type=str, default='lin_bn_lrelu_lin') + parser.add_argument('--num_struct_elements', type=int, default=128) + parser.add_argument('--cls_hidden_dimension', type=int, default=512) + parser.add_argument('--drop_out', type=float, default=0.5) + parser.add_argument('--output_dir', type=str, default='results') + parser.set_defaults(ssl=False) + + args = parser.parse_args() + print(args) + + device = args.device + dataset = dataset_factory(args.dataset_name, verbose=args.verbose) + if args.readout == "extph": + model = PershomLearnedFiltSup(dataset, args.use_super_level_set_filtration, args.use_node_degree, + args.set_node_degree_uninformative, args.use_node_label, + args.use_raw_node_label, + args.filt_conv_number, args.filt_conv_dimension, args.gin_mlp_type, + args.num_struct_elements, args.cls_hidden_dimension, args.drop_out, + conv_number=args.conv_number, conv_dimension=args.conv_dimension, aug=None, + readout=args.readout).to(device) + else: + model = ClassicReadoutFilt(dataset, args.use_super_level_set_filtration, args.use_node_degree, + args.set_node_degree_uninformative, args.use_node_label, + args.use_raw_node_label, + args.filt_conv_number, args.filt_conv_dimension, args.gin_mlp_type, + args.num_struct_elements, args.cls_hidden_dimension, args.drop_out, + conv_number=args.conv_number, conv_dimension=args.conv_dimension, aug=None, + readout=args.readout).to(device) + + cross_validation_with_val_set(args, dataset, model, 10, args.num_epochs, args.batch_size, + args.lr, lr_decay_factor=0.5, lr_decay_step_size=50, + weight_decay=0) diff --git a/train/pinwheels_train_eval.py b/train/pinwheels_train_eval.py new file mode 100644 index 0000000..5722426 --- /dev/null +++ b/train/pinwheels_train_eval.py @@ -0,0 +1,298 @@ +import time +import sys + +import torch +import torch.nn.functional as F +from torch import tensor +from torch.optim import Adam +from sklearn.model_selection import StratifiedKFold +# from torch_geometric.loader import DataLoader, DenseDataLoader as DenseLoader +from torch.utils.data import DataLoader +from torch.utils.tensorboard import SummaryWriter +writer= SummaryWriter() + +import sys +sys.path.append(".") + +import os.path as osp +import uuid +import pickle +import datetime +import random +import torch +import os + +import argparse + +from torch.utils.tensorboard import SummaryWriter +writer= SummaryWriter() + +import numpy as np + +import torch.optim as optim + +from torch.optim.lr_scheduler import MultiStepLR + +from gnn.pinwheels_model import PershomLearnedFilt, PershomLearnedFiltSup, PershomRigidDegreeFilt, GIN, SimpleNNBaseline, ClassicGNN, ClassicReadoutFilt +from data.data import dataset_factory, train_test_val_split, Subset +from data.utils import my_collate + + + +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + +class Subset(torch.utils.data.Dataset): + def __init__(self, dataset, indices): + assert isinstance(indices, (list, tuple)) + self.ds = dataset + self.indices = tuple(indices) + self.y = dataset.y[indices] + assert len(indices) <= len(dataset) + + def __len__(self): + return len(self.indices) + + def __getitem__(self, idx): + return self.ds[self.indices[idx]] + + +def cross_validation_with_val_set(args, dataset, model, folds, epochs, batch_size, + lr, lr_decay_factor, lr_decay_step_size, + weight_decay, logger=None): + + val_losses, accs, durations = [], [], [] + for fold, (train_idx, test_idx, + val_idx) in enumerate(zip(*k_fold(dataset, folds, args.device))): + # print("train_idx: ", train_idx) + tr_dataset = Subset(dataset, train_idx) + te_dataset = Subset(dataset, test_idx) + val_dataset = Subset(dataset, val_idx) + + dl_train = torch.utils.data.DataLoader( + tr_dataset, + collate_fn=my_collate, + batch_size=args.batch_size, # make this BIG for better contrastive learning + shuffle=True, + # if last batch would have size 1 we have to drop it ... + drop_last=(len(tr_dataset) % args.batch_size == 1) + ) + + dl_val = torch.utils.data.DataLoader( + val_dataset, + collate_fn=my_collate, + batch_size=1, + shuffle=False, + # drop_last = (len(dataset) % 64 == 1) + ) + dl_test = torch.utils.data.DataLoader( + te_dataset, + collate_fn=my_collate, + batch_size=1, + shuffle=False, + # drop_last = (len(dataset) % 64 == 1) + ) + model.to(device).reset_parameters() + optimizer = Adam(model.parameters(), lr=lr, weight_decay=weight_decay) + + if torch.cuda.is_available(): + torch.cuda.synchronize() + + t_start = time.perf_counter() + + for epoch in range(1, epochs + 1): + train_loss = train_ph(args, model, optimizer, dl_train) + val_losses.append(eval_loss(model, dl_val)) + accs.append(eval_acc(model, dl_test)) + eval_info = { + 'fold': fold, + 'epoch': epoch, + 'train_loss': train_loss, + 'val_loss': val_losses[-1], + 'test_acc': accs[-1], + } + print(eval_info) + writer.add_scalars('loss_' + args.exp_name, {'train_loss_fold'+str(fold): float(train_loss)}, epoch) + writer.add_scalars('acc_' + args.exp_name, {'test_acc_fold'+str(fold): float(accs[-1])}, epoch) + if logger is not None: + logger(eval_info) + + if epoch % lr_decay_step_size == 0: + for param_group in optimizer.param_groups: + param_group['lr'] = lr_decay_factor * param_group['lr'] + + if torch.cuda.is_available(): + torch.cuda.synchronize() + + t_end = time.perf_counter() + durations.append(t_end - t_start) + + loss, acc, duration = tensor(val_losses), tensor(accs), tensor(durations) + loss, acc = loss.view(folds, epochs), acc.view(folds, epochs) + loss, argmin = loss.min(dim=1) + # acc, argmax= acc.max(dim=1)### + acc = acc[torch.arange(folds, dtype=torch.long), argmin]### + loss_mean = loss.mean().item() + acc_mean = acc.mean().item() + acc_std = acc.std().item() + duration_mean = duration.mean().item() + print(f'Val Loss: {loss_mean:.4f}, Test Accuracy: {acc_mean:.3f} ' + f'± {acc_std:.3f}, Duration: {duration_mean:.3f}') + + return loss_mean, acc_mean, acc_std + + +def k_fold(dataset, folds, device): + skf = StratifiedKFold(folds, shuffle=True, random_state=12345) + + test_indices, train_indices = [], [] + print(dataset[0]) + y= torch.tensor([dataset[i].y.item() for i in range(len(dataset))], dtype= torch.long) + + for _, idx in skf.split(torch.zeros(len(dataset)), y): + test_indices.append(list(idx)) + + val_indices = [test_indices[i - 1] for i in range(folds)] + + for i in range(folds): + train_mask = torch.ones(len(dataset), dtype=torch.bool) + train_mask[test_indices[i]] = 0 + train_mask[val_indices[i]] = 0 + train_indices.append(list(train_mask.nonzero(as_tuple=False).view(-1).cpu().numpy())) + + return (list(train_indices)), (list(test_indices)), (list(val_indices)) + +def num_graphs(data): + if hasattr(data, 'num_graphs'): + return data.num_graphs + else: + return data.x.size(0) + +def train_ph(args, model, opt, dl_train): + model.train() + + total_loss = 0 + + for batch_i, batch in enumerate(dl_train, start=1): + opt.zero_grad() + batch = batch.to(device) + if not hasattr(batch, 'node_lab'): batch.node_lab = None + batch.boundary_info = [e.to(device) for e in batch.boundary_info] + + logit= (model(batch,device)).to(device) + loss = F.nll_loss(logit.float(), batch.y.view(-1).long()) + + loss.backward() + total_loss += loss.item() * num_graphs(batch) + opt.step() + return total_loss / len(dl_train.dataset) + +def train(model, optimizer, loader): + model.train() + + total_loss = 0 + for data in loader: + optimizer.zero_grad() + data = data.to(device) + out = model(data, device) + loss = F.nll_loss(out.float(), data.y.view(-1).long()) + loss.backward() + total_loss += loss.item() * num_graphs(data) + optimizer.step() + return total_loss / len(loader.dataset) + + +def eval_acc(model, loader): + model.eval() + + correct = 0 + for data in loader: + data = data.to(device) + with torch.no_grad(): + pred = model(data, device).max(1)[1] + correct += pred.eq(data.y.view(-1)).sum().item() + return correct / len(loader.dataset) + + +def eval_loss(model, loader): + model.eval() + + loss = 0 + for data in loader: + data = data.to(device) + with torch.no_grad(): + out = model(data, device) + loss += F.nll_loss(out.float(), data.y.view(-1).long(), reduction='sum').item() + return loss / len(loader.dataset) + +if __name__ == "__main__": + print("PINWHEELS TRAIN") + + parser = argparse.ArgumentParser(description='PINWHEELS') + + parser.add_argument('--device', type= int, default= 0) + + parser.add_argument('--max_process_on_device', type=int) + parser.add_argument('--readout', type=str, default="extph") + parser.add_argument('--exp_name', type= str, default= 'pinwheels', help= 'experiment name to save as') + parser.add_argument('--dataset_name', type= str, default= 'PinWheels') + # parser.add_argument('--selfsupervised', dest='ssl', default= True, type= bool)#action='store_false') + # parser.add_argument('--evaluation', type=str, default= 'SVC', choices= ['RandomForest', 'LogisticRegression', 'SVC'], help= 'downstream evaluation protocol classifier type') + parser.add_argument('--seed', dest= 'seed', default= 0) + parser.add_argument('--verbose', type= bool, default= True) + parser.add_argument('--sup_combo', dest= 'sup', default= True, type= bool) + parser.add_argument('--lr', type= float, default= 0.01) + parser.add_argument('--lr_drop_fact', type= float, default= 0.5) + parser.add_argument('--num_epochs', type=int, default=100) + parser.add_argument('--epoch_step', type=int, default=30) + parser.add_argument('--batch_size', type=int, default=128)#512) + parser.add_argument('--weight_decay', type=float, default=0.0) + parser.add_argument('--use_super_level_set_filtration', type=bool, default=True) + parser.add_argument('--use_raw_node_label', type= bool, default= False) + parser.add_argument('--use_node_degree', type=bool, default=True) + parser.add_argument('--set_node_degree_uninformative', type=bool, default=False) + parser.add_argument('--use_node_label', type=bool, default=False) + parser.add_argument('--filt_conv_number', type=int, default=1) + parser.add_argument('--filt_conv_dimension', type=int, default=128) + parser.add_argument('--conv_number', type=int, default=5) + parser.add_argument('--conv_dimension', type=int, default=128) + parser.add_argument('--gin_mlp_type', type=str, default='lin_bn_lrelu_lin') + parser.add_argument('--num_struct_elements', type=int, default=128) + parser.add_argument('--cls_hidden_dimension', type=int, default=512) + parser.add_argument('--drop_out', type=float, default=0.5) + parser.add_argument('--output_dir', type=str, default= 'results') + parser.set_defaults(ssl=False) + + args = parser.parse_args() + + device= args.device + dataset = dataset_factory(args.dataset_name, verbose=args.verbose) + + if args.readout == "extph": + model = PershomLearnedFiltSup(dataset, args.use_super_level_set_filtration, args.use_node_degree, + args.set_node_degree_uninformative, args.use_node_label, + args.use_raw_node_label, + args.filt_conv_number, args.filt_conv_dimension, args.gin_mlp_type, + args.num_struct_elements, args.cls_hidden_dimension, args.drop_out, + conv_number=args.conv_number, conv_dimension=args.conv_dimension, aug=None, + readout=args.readout).to(device) + else: + model = ClassicReadoutFilt(dataset, args.use_super_level_set_filtration, args.use_node_degree, + args.set_node_degree_uninformative, args.use_node_label, + args.use_raw_node_label, + args.filt_conv_number, args.filt_conv_dimension, args.gin_mlp_type, + args.num_struct_elements, args.cls_hidden_dimension, args.drop_out, + conv_number=args.conv_number, conv_dimension=args.conv_dimension, aug=None, + readout=args.readout).to(device) + gnn = ClassicGNN(dataset, + use_node_degree=args.use_node_degree, + set_node_degree_uninformative=args.set_node_degree_uninformative, + use_node_label=args.use_node_label, + use_raw_node_label=args.use_raw_node_label, + gin_number=args.conv_number, + conv_type='GIN', + gin_dimension=args.conv_dimension, + gin_mlp_type=args.gin_mlp_type).to(device) + cross_validation_with_val_set(args, dataset, model, 10, args.num_epochs, args.batch_size, + args.lr, lr_decay_factor=0.5, lr_decay_step_size= 50, + weight_decay=0) diff --git a/train/proteins_train_eval.py b/train/proteins_train_eval.py new file mode 100644 index 0000000..b3a1c90 --- /dev/null +++ b/train/proteins_train_eval.py @@ -0,0 +1,304 @@ +import time +import sys +import torch +import torch.nn.functional as F +from torch import tensor +from torch.optim import Adam +from sklearn.model_selection import StratifiedKFold +# from torch_geometric.loader import DataLoader, DenseDataLoader as DenseLoader +from torch.utils.data import DataLoader +from torch.utils.tensorboard import SummaryWriter +writer= SummaryWriter() + +import sys +sys.path.append(".") + +import os.path as osp +import uuid +import pickle +import datetime +import random +import torch +import os + +import argparse + +from torch.utils.tensorboard import SummaryWriter +writer= SummaryWriter() + +import numpy as np + +import torch.optim as optim + +from torch.optim.lr_scheduler import MultiStepLR + +torch.backends.cudnn.deterministic = True +# torch.manual_seed(12345) +# torch.cuda.manual_seed_all(12345) +# random.seed(12345) +# np.random.seed(12345) + +from gnn.proteins_model import PershomLearnedFilt, PershomLearnedFiltSup, PershomRigidDegreeFilt, GIN, SimpleNNBaseline, ClassicGNN, ClassicReadoutFilt +from data.data import dataset_factory, train_test_val_split, Subset +from data.utils import my_collate + + +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + +class Subset(torch.utils.data.Dataset): + def __init__(self, dataset, indices): + assert isinstance(indices, (list, tuple)) + self.ds = dataset + self.indices = tuple(indices) + self.y = dataset.y[indices] + assert len(indices) <= len(dataset) + + def __len__(self): + return len(self.indices) + + def __getitem__(self, idx): + return self.ds[self.indices[idx]] + + +def cross_validation_with_val_set(args, dataset, model, folds, epochs, batch_size, + lr, lr_decay_factor, lr_decay_step_size, + weight_decay, logger=None): + + val_losses, accs, durations = [], [], [] + for fold, (train_idx, test_idx, + val_idx) in enumerate(zip(*k_fold(dataset, folds, args.device))): + tr_dataset = Subset(dataset, train_idx) + te_dataset = Subset(dataset, test_idx) + val_dataset = Subset(dataset, val_idx) + + dl_train = torch.utils.data.DataLoader( + tr_dataset, + collate_fn=my_collate, + batch_size=args.batch_size, # make this BIG for better contrastive learning + shuffle=True, + # if last batch would have size 1 we have to drop it ... + drop_last=(len(tr_dataset) % args.batch_size == 1) + ) + + dl_val = torch.utils.data.DataLoader( + val_dataset, + collate_fn=my_collate, + batch_size=1, + shuffle=False, + # drop_last = (len(dataset) % 64 == 1) + ) + dl_test = torch.utils.data.DataLoader( + te_dataset, + collate_fn=my_collate, + batch_size=1, + shuffle=False, + # drop_last = (len(dataset) % 64 == 1) + ) + model.to(device).reset_parameters() + optimizer = Adam(model.parameters(), lr=lr, weight_decay=weight_decay) + + if torch.cuda.is_available(): + torch.cuda.synchronize() + + t_start = time.perf_counter() + + for epoch in range(1, epochs + 1): + train_loss = train_ph(args, model, optimizer, dl_train) + val_losses.append(eval_loss(model, dl_val)) + accs.append(eval_acc(model, dl_test)) + eval_info = { + 'fold': fold, + 'epoch': epoch, + 'train_loss': train_loss, + 'val_loss': val_losses[-1], + 'test_acc': accs[-1], + } + print(eval_info, flush= True) + + writer.add_scalars('loss_' + args.exp_name, {'train_loss_fold'+str(fold): float(train_loss)}, epoch) + writer.add_scalars('acc_' + args.exp_name, {'test_acc_fold'+str(fold): float(accs[-1])}, epoch) + if logger is not None: + logger(eval_info) + + if epoch % lr_decay_step_size == 0: + for param_group in optimizer.param_groups: + param_group['lr'] = lr_decay_factor * param_group['lr'] + + if torch.cuda.is_available(): + torch.cuda.synchronize() + + t_end = time.perf_counter() + durations.append(t_end - t_start) + fold_i= fold+1 + loss, acc, duration = tensor(val_losses), tensor(accs), tensor(durations) + loss, acc = loss.view(fold_i, epochs), acc.view(fold_i, epochs) + loss, argmin = loss.min(dim=1) + acc = acc[torch.arange(fold_i, dtype=torch.long), argmin] + + loss_mean = loss.mean().item() + acc_mean = acc.mean().item() + acc_std = acc.std().item() + duration_mean = duration.mean().item() + print('AFTER FOLD ' + str(fold) + f'Val Loss: {loss_mean:.4f}, Test Accuracy: {acc_mean:.3f} ' + f'± {acc_std:.3f}, Duration: {duration_mean:.3f}', flush=True) + loss, acc, duration = tensor(val_losses), tensor(accs), tensor(durations) + loss, acc = loss.view(folds, epochs), acc.view(folds, epochs) + loss, argmin = loss.min(dim=1) + acc = acc[torch.arange(folds, dtype=torch.long), argmin] + + loss_mean = loss.mean().item() + acc_mean = acc.mean().item() + acc_std = acc.std().item() + duration_mean = duration.mean().item() + print(f'Val Loss: {loss_mean:.4f}, Test Accuracy: {acc_mean:.3f} ' + f'± {acc_std:.3f}, Duration: {duration_mean:.3f}', flush= True) + + return loss_mean, acc_mean, acc_std + + +def k_fold(dataset, folds, device): + skf = StratifiedKFold(folds, shuffle=True, random_state=12345) + + test_indices, train_indices = [], [] + y= torch.tensor([dataset[i].y for i in range(len(dataset))], dtype= torch.long) + for _, idx in skf.split(torch.zeros(len(dataset)), y): + test_indices.append(list(idx)) + + val_indices = [test_indices[i - 1] for i in range(folds)] + + for i in range(folds): + train_mask = torch.ones(len(dataset), dtype=torch.bool) + train_mask[test_indices[i]] = 0 + train_mask[val_indices[i]] = 0 + # train_indices.append(train_mask.nonzero(as_tuple=False).view(-1)) + train_indices.append(list(train_mask.nonzero(as_tuple=False).view(-1).cpu().numpy())) + return (list(train_indices)), (list(test_indices)), (list(val_indices)) + +def num_graphs(data): + if hasattr(data, 'num_graphs'): + return data.num_graphs + else: + return data.x.size(0) + +def train_ph(args, model, opt, dl_train): + model.train() + + total_loss = 0 + + for batch_i, batch in enumerate(dl_train, start=1): + opt.zero_grad() + batch = batch.to(device) + if not hasattr(batch, 'node_lab'): batch.node_lab = None + batch.boundary_info = [e.to(device) for e in batch.boundary_info] + + logit= (model(batch,device)).to(device) + + loss1 = F.nll_loss(logit.float(), batch.y.view(-1).long()) + loss= loss1 + loss.backward() + total_loss += loss.item() * num_graphs(batch) + opt.step() + + return total_loss / len(dl_train.dataset) + +def train(model, optimizer, loader): + model.train() + + total_loss = 0 + for data in loader: + optimizer.zero_grad() + data = data.to(device) + out = model(data, device) + loss = F.nll_loss(out.float(), data.y.view(-1).long()) + loss.backward() + total_loss += loss.item() * num_graphs(data) + optimizer.step() + return total_loss / len(loader.dataset) + + +def eval_acc(model, loader): + model.eval() + + correct = 0 + for data in loader: + data = data.to(device) + with torch.no_grad(): + pred = model(data, device).max(1)[1] + correct += pred.eq(data.y.view(-1)).sum().item() + return correct / len(loader.dataset) + + +def eval_loss(model, loader): + model.eval() + + loss = 0 + for data in loader: + data = data.to(device) + with torch.no_grad(): + out = model(data, device) + loss += F.nll_loss(out.float(), data.y.view(-1).long(), reduction='sum').item() + return loss / len(loader.dataset) + +if __name__ == "__main__": + print("PROTEINS TRAIN") + + parser = argparse.ArgumentParser(description='PROTEINS') + parser.add_argument('--device', type= int, default= 0) + + parser.add_argument('--max_process_on_device', type=int) + parser.add_argument('--readout', type=str, default="extph") + parser.add_argument('--exp_name', type= str, default= 'protein', help= 'experiment name to save as') + parser.add_argument('--dataset_name', type= str, default= 'PROTEINS') + # parser.add_argument('--selfsupervised', dest='ssl', default= True, type= bool)#action='store_false') + # parser.add_argument('--evaluation', type=str, default= 'SVC', choices= ['RandomForest', 'LogisticRegression', 'SVC'], help= 'downstream evaluation protocol classifier type') + parser.add_argument('--seed', dest= 'seed', default= 0) + parser.add_argument('--verbose', type= bool, default= True) + parser.add_argument('--sup_combo', dest= 'sup', default= True, type= bool) + parser.add_argument('--lr', type= float, default= 0.01) + parser.add_argument('--lr_drop_fact', type= float, default= 0.5) + parser.add_argument('--num_epochs', type=int, default=50) + parser.add_argument('--epoch_step', type=int, default=30) + parser.add_argument('--batch_size', type=int, default=128)#512) + parser.add_argument('--weight_decay', type=float, default=0.0) + parser.add_argument('--use_super_level_set_filtration', type=bool, default=True) + parser.add_argument('--use_raw_node_label', type= bool, default= True) + parser.add_argument('--use_node_degree', type=bool, default=True) + parser.add_argument('--set_node_degree_uninformative', type=bool, default=False) + parser.add_argument('--use_node_label', type=bool, default=True) + parser.add_argument('--filt_conv_number', type=int, default=2) + parser.add_argument('--filt_conv_dimension', type=int, default=128) + parser.add_argument('--conv_number', type=int, default=5) + parser.add_argument('--conv_dimension', type=int, default=128) + parser.add_argument('--gin_mlp_type', type=str, default='lin_bn_lrelu_lin') + parser.add_argument('--num_struct_elements', type=int, default=64) + parser.add_argument('--cls_hidden_dimension', type=int, default=512) + parser.add_argument('--drop_out', type=float, default=0.5) + parser.add_argument('--output_dir', type=str, default= 'results') + parser.set_defaults(ssl=False) + + args = parser.parse_args() + + device= args.device + dataset = dataset_factory(args.dataset_name, verbose=args.verbose) + + if args.readout == "extph": + model = PershomLearnedFiltSup(dataset, args.use_super_level_set_filtration, args.use_node_degree, + args.set_node_degree_uninformative, args.use_node_label, + args.use_raw_node_label, + args.filt_conv_number, args.filt_conv_dimension, args.gin_mlp_type, + args.num_struct_elements, args.cls_hidden_dimension, args.drop_out, + conv_number=args.conv_number, conv_dimension=args.conv_dimension, aug=None, + readout=args.readout).to(device) + else: + model = ClassicReadoutFilt(dataset, args.use_super_level_set_filtration, args.use_node_degree, + args.set_node_degree_uninformative, args.use_node_label, + args.use_raw_node_label, + args.filt_conv_number, args.filt_conv_dimension, args.gin_mlp_type, + args.num_struct_elements, args.cls_hidden_dimension, args.drop_out, + conv_number=args.conv_number, conv_dimension=args.conv_dimension, aug=None, + readout=args.readout).to(device) + + cross_validation_with_val_set(args, dataset, model, 10, args.num_epochs, args.batch_size, + args.lr, lr_decay_factor=0.5, lr_decay_step_size= 50, + weight_decay=0)

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